I introduce a paper “An incremental algorithm for transition-based CCG parsing” by Bharat Ram Ambati, Tejaswini Deoskar, Mark Johnson and Mark Steedman.
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
Advanced atpg based on fan, testability measures and fault reductionVLSICS Design
A new algorithm based on Input Vector Control (IVC) technique is proposed, which shifts logic gate of a
circuit to its minimum leakage state, when device goes into its idle state. Leakage current in CMOS VLSI
circuit has become a major constrain in a battery operated device for technology node below 90nm, as it
drains the battery even when a circuit is in standby mode. Major concern is the leakage even in run time
condition, here aim is to focus on run time leakage reduction technique of integrated Circuit. It is inherited
by stacking effect when the series transistors are maximized in OFF state condition. This method is
independent of process technology and does not require any additional power supply. This paper gives an
optimized solution of input pattern determination of some small circuit to find minimum leakage vector
considering promising and non-promising node which helps to reduce the time complexity of the algorithm.
Proposed algorithm is simulated using HSPICE simulator for 2 input NAND gate and different standard
logic cells and achieved 94.2% and 54.59 % average leakage power reduction for 2 input NAND cell and
different logics respectively.
This document describes research on efficient data structures and algorithms for solving range aggregate problems. It begins with introductions to computational geometry and classic problems in the field like finding the closest pair of points. It then discusses concepts like output sensitivity and different computation models. Range searching data structures like range trees are described for solving problems like orthogonal range queries. The document outlines solving problems related to planar range maxima and planar range convex hull queries. It proposes preprocessing point data to speed up queries for problems like reporting the skyline points within a 2-sided range.
DNA Compression (Encoded using Huffman Encoding Method)Marwa Al-Rikaby
The document discusses DNA compression algorithms. It describes the common components of most DNA compression algorithms, which include finding repeat segments in a DNA sequence, considering approximate repeats allowing for operations like substitutions, and selecting the best set of compatible repeats. It then provides an example demonstrating how these steps may be applied to a sample DNA sequence to identify repeat segments and encode them for compression.
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
Advanced atpg based on fan, testability measures and fault reductionVLSICS Design
A new algorithm based on Input Vector Control (IVC) technique is proposed, which shifts logic gate of a
circuit to its minimum leakage state, when device goes into its idle state. Leakage current in CMOS VLSI
circuit has become a major constrain in a battery operated device for technology node below 90nm, as it
drains the battery even when a circuit is in standby mode. Major concern is the leakage even in run time
condition, here aim is to focus on run time leakage reduction technique of integrated Circuit. It is inherited
by stacking effect when the series transistors are maximized in OFF state condition. This method is
independent of process technology and does not require any additional power supply. This paper gives an
optimized solution of input pattern determination of some small circuit to find minimum leakage vector
considering promising and non-promising node which helps to reduce the time complexity of the algorithm.
Proposed algorithm is simulated using HSPICE simulator for 2 input NAND gate and different standard
logic cells and achieved 94.2% and 54.59 % average leakage power reduction for 2 input NAND cell and
different logics respectively.
This document describes research on efficient data structures and algorithms for solving range aggregate problems. It begins with introductions to computational geometry and classic problems in the field like finding the closest pair of points. It then discusses concepts like output sensitivity and different computation models. Range searching data structures like range trees are described for solving problems like orthogonal range queries. The document outlines solving problems related to planar range maxima and planar range convex hull queries. It proposes preprocessing point data to speed up queries for problems like reporting the skyline points within a 2-sided range.
DNA Compression (Encoded using Huffman Encoding Method)Marwa Al-Rikaby
The document discusses DNA compression algorithms. It describes the common components of most DNA compression algorithms, which include finding repeat segments in a DNA sequence, considering approximate repeats allowing for operations like substitutions, and selecting the best set of compatible repeats. It then provides an example demonstrating how these steps may be applied to a sample DNA sequence to identify repeat segments and encode them for compression.
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
In this work, differential evolution based compressive sensing technique for detection of faulty sensors in linear arrays has been presented. This algorithm starts from taking the linear measurements of the power pattern generated by the array under test. The difference between the collected compressive measurements and measured healthy array field pattern is minimized using a hybrid differential evolution (DE). In the proposed method, the slow convergence of DE based compressed sensing technique is accelerated with the help of parallel coordinate decent algorithm (PCD). The combination of DE with PCD makes the minimization faster and precise. Simulation results validate the performance to detect faulty sensors from a small number of measurements.
Concepts of Temporal CNN, Recurrent Neural Network, AttentionSaumyaMundra3
The document discusses and compares various neural network architectures for sequential data including RNNs, LSTMs, GRUs, Temporal CNNs, and attention mechanisms. RNNs use sequential memory to capture patterns in sequences but can suffer from vanishing gradients. LSTMs and GRUs address this issue with gating mechanisms. Temporal CNNs use techniques like causal convolutions, dilated filters, and residual blocks to process sequential data while CNNs are designed for grid-like data. Attention mechanisms allow networks to focus on important parts of the input sequence.
Adaptive modified backpropagation algorithm based on differential errorsIJCSEA Journal
A new efficient modified back propagation algorithm with adaptive learning rate is proposed to increase the convergence speed and to minimize the error. The method eliminates initial fixing of learning rate through trial and error and replaces by adaptive learning rate. In each iteration, adaptive learning rate for output and hidden layer are determined by calculating differential linear and nonlinear errors of output layer and hidden layer separately. In this method, each layer has different learning rate in each iteration. The performance of the proposed algorithm is verified by the simulation results.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
The document summarizes and compares several algorithms for signal approximation and sparse signal recovery, including Equivalent Detection (ED), Non-negative Equivalent Detection, Orthogonal Matching Pursuit (OMP), and Stagewise Orthogonal Matching Pursuit (StOMP). It discusses how each algorithm works, including iteratively selecting atoms from a dictionary to build up a sparse representation of the signal. OMP selects one atom per iteration while StOMP selects all atoms above a threshold. The document also discusses computational complexities of the different algorithms.
Artificial Intelligence Applications in Petroleum Engineering - Part IRamez Abdalla, M.Sc
This document discusses applications of artificial intelligence, specifically artificial neural networks and genetic algorithms, in petroleum engineering. It provides an overview of neural networks in OnePetro papers, describes the basic concepts and training processes of neural networks and genetic algorithms. It then discusses various applications of these techniques in reservoir engineering, production technologies, and oil well drilling, including reservoir characterization, modeling, well test analysis, permeability prediction, production monitoring, drilling optimization, and more. The presentation aims to explore these applications in more depth.
Disease Classification using ECG Signal Based on PCA Feature along with GA & ...IRJET Journal
This document describes a method for classifying ECG signals to detect cardiovascular diseases using principal component analysis (PCA), genetic algorithms, and artificial neural networks. PCA is used to extract features from ECG signals. A genetic algorithm is then used to select optimal features and train an artificial neural network classifier. The method is tested on datasets from Physionet.org to classify ECG signals as normal or indicating conditions like bradycardia or tachycardia with high accuracy. The goal is to develop an automated system for ECG analysis and heart disease diagnosis.
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large neural network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple simpler networks instead of one complex network for classification.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...iosrjce
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple smaller networks over a single large network for classification.
This document discusses performance of matching algorithms for signal approximation. It begins by introducing matching pursuit algorithms like Orthogonal Matching Pursuit (OMP) and Stagewise Orthogonal Matching Pursuit (StOMP) which are greedy algorithms that approximate sparse signals. It then describes the Non-Negative Least Squares algorithm which solves non-negative least squares problems. Finally, it discusses Extranious Equivalent Detection (EED), a modification of OED that incorporates non-negativity of representations by using a non-negative optimization technique instead of orthogonal projection.
The document discusses several approaches for learning molecular representations using deep learning networks, including:
1. Improved Neural Fingerprint (A-NFP), which addresses limitations in the original Neural Fingerprint approach by differentiating between bond types. A-NFP achieves better performance than NFP on toxicity, solubility, and drug efficacy prediction tasks.
2. Message Passing Neural Networks (MPNN), which provide a general framework for graph-structured data like molecules. MPNNs contain message passing and readout phases. CNNs for learning molecular fingerprints is a specific case of MPNNs.
3. Mol2vec, which learns molecular representations without labels using a skip-gram model similar to word2vec and
EE 567ProjectDue Tuesday, December 3, 2019 at 640 p.m..docxgidmanmary
EE 567
Project
Due Tuesday, December 3, 2019 at 6:40 p.m.
Work all 3 Parts.
Instructions.
Your project should be typed on one side of the paper only and stapled
in the upper left hand corner. You should include a cover page and an
appendix where you include your Matlab code. Do not place your project
inside any kind of binder. This is to be an individual effort. You may consult
any written material (hard or soft copy) but you may not solicit input from
any person except that you may ask the professor or TA questions regard-
ing your project. Your project report should be self-contained, that is, the
reader should be able to understand the problems and your solutions without
consulting the actual project assignment.
Part 1.
Assume we have downconverted a received signal via a mixing operation
and now we wish to apply a LPF. One way to implement a LPF is simply to
compute an average. In continuous time we would just integrate the signal
since dividing by the integration time T to compute the average would not
affect the performance since the noise would be scaled by the same amount
as the signal component. In discrete time we would implement a sum instead
of an integral. For this part we will assume we have a discrete time signal
but we will compute an average instead of just a sum.
So let us assume the input to the LPF is a signal of the form
s(k) =
√
E + double frequency terms + n(k), k = 0, 1, . . .
where we have assumed scaling so that n(k) is a standard normal random
variable for each k and ni is independent of nj for i 6= j, i, j = 0, 1, . . ..
We may ignore the double frequency terms and assume they are suppressed,
either completely or at least sufficiently, by the LPF. The output of the LPF
1
is
y(n) =
1
N
n
∑
k=n−N +1
s(k), n = 0, 1 . . .
where we take y(n) = 0 for n < 0.
Even though we are computing an average we will refer to this type of filter
as an integrate and dump or I&D filter.
Now for implementation purposes we can also construct a LPF using an
IIR filter. Let
ỹ(n) = (1 − α)s(n) + αỹ(n − 1), n = 0, 1 . . .
where we take ỹ(−1) = 0.
a. Determine (analytically) the value of α = α(N ) so that the mean and
variance of the IIR filter output matches the mean and variance of the
I&D filter output as n → ∞. For this task you may assume without
loss of generality that you only have noise present.
b. Compute (analytically) the impulse response of the I&D filter.
c. Compute (analytically) the step response of the I&D filter.
d. Compute (analytically) the impulse response of the IIR filter using the
value of α found in part (a).
e. Compute (analytically) the step response of the IIR filter using the
value of α found in part (a).
f. Plot the impulse response for each filter on the same graph using N = 8.
g. Plot the step response for each filter on the same graph using N = 8.
Part 2.
Assume we have downconverted a BPSK signal such that the input to a
LPF is of the form
s(k) = A + double frequency terms ...
Gene's law, Common gate, kernel Principal Component Analysis, ASIC Physical Design Post-Layout Verification, TSMC180nm, 0.13um IBM CMOS technology, Cadence Virtuoso, FPAA, in Spanish, Bruun E,
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Large Scale Kernel Learning using Block Coordinate DescentShaleen Kumar Gupta
This paper explores using block coordinate descent to scale kernel learning methods to large datasets. It compares exact kernel methods to two approximation techniques, Nystrom and random Fourier features, on speech, text, and image datasets. Experimental results show that Nystrom generally achieves better accuracy than random features but requires more iterations. The paper also analyzes the performance and scalability of computing kernel blocks in a distributed setting.
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTIONIJCSEA Journal
Stock market price index prediction is a challenging task for investors and scholars. Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions. The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose. It is found that numerical algorithm outperform heuristic techniques.
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
In this work, differential evolution based compressive sensing technique for detection of faulty sensors in linear arrays has been presented. This algorithm starts from taking the linear measurements of the power pattern generated by the array under test. The difference between the collected compressive measurements and measured healthy array field pattern is minimized using a hybrid differential evolution (DE). In the proposed method, the slow convergence of DE based compressed sensing technique is accelerated with the help of parallel coordinate decent algorithm (PCD). The combination of DE with PCD makes the minimization faster and precise. Simulation results validate the performance to detect faulty sensors from a small number of measurements.
Concepts of Temporal CNN, Recurrent Neural Network, AttentionSaumyaMundra3
The document discusses and compares various neural network architectures for sequential data including RNNs, LSTMs, GRUs, Temporal CNNs, and attention mechanisms. RNNs use sequential memory to capture patterns in sequences but can suffer from vanishing gradients. LSTMs and GRUs address this issue with gating mechanisms. Temporal CNNs use techniques like causal convolutions, dilated filters, and residual blocks to process sequential data while CNNs are designed for grid-like data. Attention mechanisms allow networks to focus on important parts of the input sequence.
Adaptive modified backpropagation algorithm based on differential errorsIJCSEA Journal
A new efficient modified back propagation algorithm with adaptive learning rate is proposed to increase the convergence speed and to minimize the error. The method eliminates initial fixing of learning rate through trial and error and replaces by adaptive learning rate. In each iteration, adaptive learning rate for output and hidden layer are determined by calculating differential linear and nonlinear errors of output layer and hidden layer separately. In this method, each layer has different learning rate in each iteration. The performance of the proposed algorithm is verified by the simulation results.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
The document summarizes and compares several algorithms for signal approximation and sparse signal recovery, including Equivalent Detection (ED), Non-negative Equivalent Detection, Orthogonal Matching Pursuit (OMP), and Stagewise Orthogonal Matching Pursuit (StOMP). It discusses how each algorithm works, including iteratively selecting atoms from a dictionary to build up a sparse representation of the signal. OMP selects one atom per iteration while StOMP selects all atoms above a threshold. The document also discusses computational complexities of the different algorithms.
Artificial Intelligence Applications in Petroleum Engineering - Part IRamez Abdalla, M.Sc
This document discusses applications of artificial intelligence, specifically artificial neural networks and genetic algorithms, in petroleum engineering. It provides an overview of neural networks in OnePetro papers, describes the basic concepts and training processes of neural networks and genetic algorithms. It then discusses various applications of these techniques in reservoir engineering, production technologies, and oil well drilling, including reservoir characterization, modeling, well test analysis, permeability prediction, production monitoring, drilling optimization, and more. The presentation aims to explore these applications in more depth.
Disease Classification using ECG Signal Based on PCA Feature along with GA & ...IRJET Journal
This document describes a method for classifying ECG signals to detect cardiovascular diseases using principal component analysis (PCA), genetic algorithms, and artificial neural networks. PCA is used to extract features from ECG signals. A genetic algorithm is then used to select optimal features and train an artificial neural network classifier. The method is tested on datasets from Physionet.org to classify ECG signals as normal or indicating conditions like bradycardia or tachycardia with high accuracy. The goal is to develop an automated system for ECG analysis and heart disease diagnosis.
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large neural network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple simpler networks instead of one complex network for classification.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...iosrjce
This document proposes a new classifier based on recurrent neural networks using multiple binary-output networks. Instead of one large network with many outputs, it uses multiple simple recurrent neural networks, each trained on a single class and outputting a binary true/false prediction. A decision layer is added to each network to determine the final classification from the sequence of outputs. The method is tested on a database of 17,000 handwritten Iranian city names, achieving a top-1 classification rate of 83.9% and average reliability of 72.3%. Experimental results show the effectiveness of using multiple smaller networks over a single large network for classification.
This document discusses performance of matching algorithms for signal approximation. It begins by introducing matching pursuit algorithms like Orthogonal Matching Pursuit (OMP) and Stagewise Orthogonal Matching Pursuit (StOMP) which are greedy algorithms that approximate sparse signals. It then describes the Non-Negative Least Squares algorithm which solves non-negative least squares problems. Finally, it discusses Extranious Equivalent Detection (EED), a modification of OED that incorporates non-negativity of representations by using a non-negative optimization technique instead of orthogonal projection.
The document discusses several approaches for learning molecular representations using deep learning networks, including:
1. Improved Neural Fingerprint (A-NFP), which addresses limitations in the original Neural Fingerprint approach by differentiating between bond types. A-NFP achieves better performance than NFP on toxicity, solubility, and drug efficacy prediction tasks.
2. Message Passing Neural Networks (MPNN), which provide a general framework for graph-structured data like molecules. MPNNs contain message passing and readout phases. CNNs for learning molecular fingerprints is a specific case of MPNNs.
3. Mol2vec, which learns molecular representations without labels using a skip-gram model similar to word2vec and
EE 567ProjectDue Tuesday, December 3, 2019 at 640 p.m..docxgidmanmary
EE 567
Project
Due Tuesday, December 3, 2019 at 6:40 p.m.
Work all 3 Parts.
Instructions.
Your project should be typed on one side of the paper only and stapled
in the upper left hand corner. You should include a cover page and an
appendix where you include your Matlab code. Do not place your project
inside any kind of binder. This is to be an individual effort. You may consult
any written material (hard or soft copy) but you may not solicit input from
any person except that you may ask the professor or TA questions regard-
ing your project. Your project report should be self-contained, that is, the
reader should be able to understand the problems and your solutions without
consulting the actual project assignment.
Part 1.
Assume we have downconverted a received signal via a mixing operation
and now we wish to apply a LPF. One way to implement a LPF is simply to
compute an average. In continuous time we would just integrate the signal
since dividing by the integration time T to compute the average would not
affect the performance since the noise would be scaled by the same amount
as the signal component. In discrete time we would implement a sum instead
of an integral. For this part we will assume we have a discrete time signal
but we will compute an average instead of just a sum.
So let us assume the input to the LPF is a signal of the form
s(k) =
√
E + double frequency terms + n(k), k = 0, 1, . . .
where we have assumed scaling so that n(k) is a standard normal random
variable for each k and ni is independent of nj for i 6= j, i, j = 0, 1, . . ..
We may ignore the double frequency terms and assume they are suppressed,
either completely or at least sufficiently, by the LPF. The output of the LPF
1
is
y(n) =
1
N
n
∑
k=n−N +1
s(k), n = 0, 1 . . .
where we take y(n) = 0 for n < 0.
Even though we are computing an average we will refer to this type of filter
as an integrate and dump or I&D filter.
Now for implementation purposes we can also construct a LPF using an
IIR filter. Let
ỹ(n) = (1 − α)s(n) + αỹ(n − 1), n = 0, 1 . . .
where we take ỹ(−1) = 0.
a. Determine (analytically) the value of α = α(N ) so that the mean and
variance of the IIR filter output matches the mean and variance of the
I&D filter output as n → ∞. For this task you may assume without
loss of generality that you only have noise present.
b. Compute (analytically) the impulse response of the I&D filter.
c. Compute (analytically) the step response of the I&D filter.
d. Compute (analytically) the impulse response of the IIR filter using the
value of α found in part (a).
e. Compute (analytically) the step response of the IIR filter using the
value of α found in part (a).
f. Plot the impulse response for each filter on the same graph using N = 8.
g. Plot the step response for each filter on the same graph using N = 8.
Part 2.
Assume we have downconverted a BPSK signal such that the input to a
LPF is of the form
s(k) = A + double frequency terms ...
Gene's law, Common gate, kernel Principal Component Analysis, ASIC Physical Design Post-Layout Verification, TSMC180nm, 0.13um IBM CMOS technology, Cadence Virtuoso, FPAA, in Spanish, Bruun E,
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Large Scale Kernel Learning using Block Coordinate DescentShaleen Kumar Gupta
This paper explores using block coordinate descent to scale kernel learning methods to large datasets. It compares exact kernel methods to two approximation techniques, Nystrom and random Fourier features, on speech, text, and image datasets. Experimental results show that Nystrom generally achieves better accuracy than random features but requires more iterations. The paper also analyzes the performance and scalability of computing kernel blocks in a distributed setting.
A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTIONIJCSEA Journal
Stock market price index prediction is a challenging task for investors and scholars. Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions. The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose. It is found that numerical algorithm outperform heuristic techniques.
Similar to An incremental algorithm for transition-based CCG parsing (20)
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
An incremental algorithm for transition-based CCG parsing
1. an incremental algorithm for
transition-based ccg parsing
B. R. Ambati, T. Deoskar, M. Johnson and M. Steedman
Miyazawa Akira
January 22, 2016
The Graduate University For Advanced Studies / National Institute of Informatics
2. incrememtal parser
What does “Incremental” mean?
An incremental parser computes the relationship between words
as soon as it receives them from the input.
Why is incrementality important?
∙ Statistical Machine Translation
∙ Automatic Speech Recognition
1
3. baseline algorithm
Their baseline algorithm NonInc is based on Zhang and Clark (2011).
It has four actions.
∙ Shift
Push a word from the input buffer to the stack and assign it with a
CCG category.
∙ Reduce Left
Pop the top two nodes from the stack, combine them into a new
node and push it back onto the stack with a new category. The
right node is the head and the left node is reduced.
∙ Reduce Right
This is similar to the action above except that the left node is the
head and the right node is reduced.
∙ Unary
Change the category of the top node on the stack.
2
17. problem in noninc
The algorithm above is not incremental. The dependncy graph starts
to grow only after almost all the words are pushed to the stack.
To solve this problem, they introduce a revealing technique
(Pareschi and Steedman (1987)).
4
32. revealing actions
1. Left Reveal (LRev)
Pop the top two nodes in the stack (left, right). Identify the left
node’s child with a subject dependency. Abstract over this child
node and split the category of left node into two categories.
Combine the nodes using CCG combinators accodingly.
Slikes (SNP)(SNP)madly
R<
NPJohn SNPlikes
<
SNP
<
S
6
33. revealing actions
2. Right Reveal (RRev)
Pop the top two nodes in the stack (left, right). Check the right
periphery of the left node in the dependency graph, extract all the
nodes with compatible CCG categories and identify all the possible
nodes that the right node can combine with. Abstract over this
node, split the category into two categories accordingly and
combine the nodes using CCG combinators.
Slikes NPNPfrom
R>
S/NPlikes NPmangoes
<
NP
>
S
7
35. features
∙ NonInc
Features based on the top four nodes in the stack and the next four words in the input buffer.
feature templates
1 S0wp, S0c, S0pc, S0wc, S1wp, S1c, S1pc, S1wc, S2pc, S2wc, S3pc, S3wc,
2 Q0wp, Q1wp, Q2wp, Q3wp,
3 S0Lpc, S0Lwc, S0Rpc, S0Rwc, S0Upc, S0Uwc, S1Lpc, S1Lwc, S1Rpc, S1Rwc, S1Upc,
S1Uwc,
4 S0wcS1wc, S0cS1w, S0wS1c, S0cS1c, S0wcQ0wp, S0cQ0wp, S0wcQ0p, S0cQ0p,
S1wcQ0wp, S1cQ0wp, S1wcQ0p, S1cQ0p,
5 S0wcS1cQ0p, S0cS1wcQ0p, S0cS1cQ0wp, S0cS1cQ0p, S0pS1pQ0p, S0wcQ0pQ1p,
S0cQ0wpQ1p, S0cQ0pQ1wp, S0cQ0pQ1p, S0pQ0pQ1p, S0wcS1cS2c, S0cS1wcS2c,
S0cS1cS2wc, S0cS1cS2, S0pS1pS2,
6 S0cS0HcS0Lc, S0cS0HcS0Rc, S1cS1HcS1Rc, S0cS0RcQ0p, S0cS0RcQ0w, S0cS0LcS1c,
S0cS0LcS1w, S0cS1cS1Rc, S0wS1cS1Rc,
∙ RevInc
↑ + B1c and B1cS0c, where B1 is the bottom most node in the right periphery.
9
36. measures of incrementality
Measures of incrementality:
∙ Connectedness
the average number of nodes in the stack before shifting
∙ Waiting time
the number of nodes that need to be shifted to the stack before a
dependency between any two nodes in the stack is resolved
Table 1: Connectedness and waiting time.
Algorithm Connectedness Waiting Time
NonInc 4.62 2.98
RevInc 2.15 0.69
10
37. results and analysis
Table 2: Performance on the development data1
.
Algorithm UP UR UF LP LR LF Cat Acc.
NonInc (beam=1) 92.57 82.60 87.30 85.12 75.96 80.28 91.10
RevInc (beam=1) 91.62 85.94 88.69 83.42 78.25 80.75 90.87
NonInc (beam=16) 92.71 89.66 91.16 85.78 82.96 84.35 92.51
Z&C (beam=16) - - - 87.15 82.95 85.00 92.77
∙ NonInc gets higher precision because it can use more context
while making a decision.
∙ RevInc achieves higher recall because information on nodes is
available even after they are reduced.
1‘U’ stands for unlabaled and ‘L’ stands for labeles. ‘P’, ‘R’ and ‘F’ are precision, recall
and F-score respectively.
11
38. results and analysis
Table 3: Label-wise F-score of RevInc and NonInc parsers.
Category RevInc NonInc
(NPNP)/NP 81.36 83.21
(NPNP)/NP 78.66 82.94
((SNP)(SNP))/NP 65.09 66.98
((SNP)(SNP))/NP 62.69 65.89
(S[dcl]NP)/NP 78.96 78.29
(S[dcl]NP)/NP 76.71 75.22
(SNP)(SNP) 80.49 76.90
∙ NonInc performs better in labels corresponding to PP due to the
availability of more context.
∙ RevInc has advantage in the case of verbal arguments and verbal
modifiers as the effect of “reveal” actions.
12
39. parsing speed
Parsing speed:
∙ NonInc parses 110 sentences/sec.
∙ RevInc parses 125 sentences/sec.
Significant amount of parsing time is spent on the feature extraction
step. But in RevInc, usually only two nodes have their feature
extracted because connectedness = 2.15, while all four nodes have to
be processed in NonInc (connectedness = 4.62).
Complex actions, LRev and RRev are rarely used (5%).
13
40. results and analysis
Table 4: Performance on the test data.
Algorithm UP UR UF LP LR LF Cat Acc.
NonInc (beam=1) 92.45 82.16 87.00 85.59 76.06 80.55 91.39
RevInc (beam=1) 91.83 86.35 89.00 84.02 79.00 81.43 91.17
NonInc (beam=16) 92.68 89.57 91.10 86.20 83.32 84.74 92.70
Z&C (beam=16) - - - 87.43 83.61 85.48 93.12
Hassan et al. 09 - - 86.31 - - - -
F-scores are improved compared to NonInc in both unlabaled and
labeled cases.
14
41. future plan
∙ Use information about lexical category probabilities (Auli and
Lopez (2011)).
∙ Explore the limited use of a beam to handle lexical ambiguity.
∙ Use a dynamic oracle strategy (Xu et al. (2014)).
∙ Apply the method to SMT and ASR.
15
42. conclusion
∙ They designed and implemented an incremental CCG parser by
introducing a technique called revealing.
∙ The parser got high scores in two measures of incrementality:
connectedness and waiting time.
∙ It performs better in parsing in the view of recall and F-score.
(labeled: +0.88%, unlabeld: +2.0%)
∙ It parses sentences faster.
16
43. references
Ambati, R. B., Deoskar, T., Johnson, M., and Steedman, M. (2015). An
incremental algorithm for transition-based CCG parsing. In
Proceedings of the 2015 Conference of NAACL, pages 53–63.
Auli, M. and Lopez, A. (2011). A comparison of loopy belief
propagation and dual decomposition for integrated CCG
supertagging and parsing. In Proceedings of the 49th Annual
Meeting of ACL, pages 470–480.
Pareschi, R. and Steedman, M. (1987). A lazy way to chart-parse with
categorial grammars. In Proceedings of the 25th Annual Meeting of
ACL.
Xu, W., Clark, S., and Zhang, Y. (2014). Shift-reduce CCG parsing with a
dependency model. In Proceedings of the 52nd Annual Meeting of
ACL, pages 218–227.
Zhang, Y. and Clark, S. (2011). Shift-reduce CCG parsing. In
Proceedings of the 49th Annual Meeting of ACL, pages 683–692.
17