In this paper a new mixed nodal-mesh formulation of the PEEC
method is proposed. Based on the hypothesis that charges reside
only on the surface of conductors and that current density is
solenoidal inside them, a novel scheme is developed fully
exploiting the physical properties of charges and currents. It
comes out that the presented approach allows to reduce the number
of unknowns while preserving the accuracy. An elegant and
efficient algorithm, based on graph theory, is proposed to
automatically search independent loops on three dimensional
rectangular grids such as those arising in volumetric PEEC
formulation. The method is validated through numerical results
that confirm the accuracy of the proposed formulation from
DC-to-daylight and its capability to provide memory saving.
In this paper a new mixed nodal-mesh formulation of the PEEC
method is proposed. Based on the hypothesis that charges reside
only on the surface of conductors and that current density is
solenoidal inside them, a novel scheme is developed fully
exploiting the physical properties of charges and currents. It
comes out that the presented approach allows to reduce the number
of unknowns while preserving the accuracy. An elegant and
efficient algorithm, based on graph theory, is proposed to
automatically search independent loops on three dimensional
rectangular grids such as those arising in volumetric PEEC
formulation. The method is validated through numerical results
that confirm the accuracy of the proposed formulation from
DC-to-daylight and its capability to provide memory saving.
A novel transform for fast detection of naturally curved items in digital images is described in this article. This general purpose image transform is defined to suit platforms with limited memory and processing footprints by utilizing only additions and simple shift and bitwise operations. We present this unique algorithmic approach in application to real world problems of iris detection and handwriting recognition systems as typical applications in such devices. The new approach is tested on several data sets and the experiments show promising and superior performance compared to existing techniques.
https://www.researchgate.net/publication/290691433_Systems_and_methods_for_obtaining_structural_information_from_a_digital_image
System and Method for Exchanging Assets in a Network - A first negotiation (142) is performed between a first agent (112) and a second agent (122). Responsive to the outcome of the first negotiation (142), a first asset is selectively purchased from the second agent (122). A second negotiation (148) is performed with a third agent (136), and, responsive to the outcome of the second negotiation (148), a second asset controlled by the first agent (112) is selectively sold to the third agent (136).
Conventional power amplifier systems trade-off efficiency for linearity due to transistor operation limitations. The Smart Power Amplifier architecture improves RF Power Amplifier linearity without compromising efficiency. This novel invention generates and utilizes specialized outphased signals that can be used in outphasing RF power amplifiers in order to amplify the input signal without suffering from the nonlinearities and inefficiencies associated with conventional power amplifiers. Using this proposed technique, designers and developers can generate variable number of outphased signals on the fly with minimal processing and memory footprints. The proposed architecture for constructing and combining a variable number of constant amplitude outphased signals results in both highly linear and significantly efficient power amplification system. The complexities of both constructing and combining the decomposed signals are significantly reduced due to the fact that all of the decomposed signals except only two of them are actually scaled versions of the input signal.
Relaxation Methods and Means for Optical Tracking of Deformable ObjectsMagdi Mohamed
There is prior art in tracking objects that uses statistical techniques such as hidden Markov models for incorporating temporal context between successive image frames. The existing approaches segment each image frame independently, using only spatial context derived from a combination of edge, color, and texture features. The temporal context, provided by history information, is considered lately, after segmentation, in the analysis phase. Since segmentation is ambiguous and prone to failure, these approaches are not suitable for tracking non-rigid and highly deformable objects. The technique being described here is an efficient method that formulates the optical tracking of deformable objects task as a probabilistic relaxation labeling process in which the current frame image is used for initializing the pixel membership probabilities, and the previous frame image is used for estimating the compatibility parameters to be utilized for segmenting the current frame image by iteratively refining its membership probabilities, incorporating both temporal and spatial contexts simultaneously, in realtime.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter Lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Q-filter Structures for Advancing Pattern Recognition SystemsMagdi Mohamed
An advanced approach for adaptive nonlinear digital data processing is described in this presentation. Three primal computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets to enable applications in mobile communication systems and the experiments show promising results.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
A novel transform for fast detection of naturally curved items in digital images is described in this article. This general purpose image transform is defined to suit platforms with limited memory and processing footprints by utilizing only additions and simple shift and bitwise operations. We present this unique algorithmic approach in application to real world problems of iris detection and handwriting recognition systems as typical applications in such devices. The new approach is tested on several data sets and the experiments show promising and superior performance compared to existing techniques.
https://www.researchgate.net/publication/290691433_Systems_and_methods_for_obtaining_structural_information_from_a_digital_image
System and Method for Exchanging Assets in a Network - A first negotiation (142) is performed between a first agent (112) and a second agent (122). Responsive to the outcome of the first negotiation (142), a first asset is selectively purchased from the second agent (122). A second negotiation (148) is performed with a third agent (136), and, responsive to the outcome of the second negotiation (148), a second asset controlled by the first agent (112) is selectively sold to the third agent (136).
Conventional power amplifier systems trade-off efficiency for linearity due to transistor operation limitations. The Smart Power Amplifier architecture improves RF Power Amplifier linearity without compromising efficiency. This novel invention generates and utilizes specialized outphased signals that can be used in outphasing RF power amplifiers in order to amplify the input signal without suffering from the nonlinearities and inefficiencies associated with conventional power amplifiers. Using this proposed technique, designers and developers can generate variable number of outphased signals on the fly with minimal processing and memory footprints. The proposed architecture for constructing and combining a variable number of constant amplitude outphased signals results in both highly linear and significantly efficient power amplification system. The complexities of both constructing and combining the decomposed signals are significantly reduced due to the fact that all of the decomposed signals except only two of them are actually scaled versions of the input signal.
Relaxation Methods and Means for Optical Tracking of Deformable ObjectsMagdi Mohamed
There is prior art in tracking objects that uses statistical techniques such as hidden Markov models for incorporating temporal context between successive image frames. The existing approaches segment each image frame independently, using only spatial context derived from a combination of edge, color, and texture features. The temporal context, provided by history information, is considered lately, after segmentation, in the analysis phase. Since segmentation is ambiguous and prone to failure, these approaches are not suitable for tracking non-rigid and highly deformable objects. The technique being described here is an efficient method that formulates the optical tracking of deformable objects task as a probabilistic relaxation labeling process in which the current frame image is used for initializing the pixel membership probabilities, and the previous frame image is used for estimating the compatibility parameters to be utilized for segmenting the current frame image by iteratively refining its membership probabilities, incorporating both temporal and spatial contexts simultaneously, in realtime.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter Lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Q-filter Structures for Advancing Pattern Recognition SystemsMagdi Mohamed
An advanced approach for adaptive nonlinear digital data processing is described in this presentation. Three primal computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets to enable applications in mobile communication systems and the experiments show promising results.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Q-Metric Based Support Vector Machines
1. Q-Metric Based
Support Vector Machines
Dimension2
dp=infinity = 1 dλ=-1 = 1
de = dp=2 = 1 y=(y1,y2)
--- advances in kernel based machine learning systems ---
Dimension1
x=(x1,x2)
dλε(-1,0) = 1
dt = dp=1 = 1 dλ=0 = 1
Graph of d(x,y)=1 in 2-D Space
Method for Constructing Q-Metric Based
Support Vector Classification and Regression Machines
2009:01:24 Magdi A. Mohamed 1/23
2. Metrics
“distance indicators”
λ=-1
Max
P=INF
Euclidian
Q-Metrics P-Metrics P=2
Euclidian
Mainstream
Manhattan
Approaches
λ=0 P=1
ge
Probability
era
Av
Measures
Plausibility
ion
Believe Probability
λ=INF
ect
“confidence quantifiers”
ers
Int
ge
Q-Aggregates
er a
λ=0
Av
ion
Un
λ=-1
−1<λ<0 λ>0
λ=0
Aggregates Q-Measures
“connective operators”
2009:01:24 Magdi A. Mohamed 2/23
3. Overview
frontiers on nonlinear modeling techniques
Nonlinear
Models
Motivating Object Image Pattern
Applications Tracking Processing Recognition
Fundamental Probability Robust Neural
Theories Measure Estimation Networks
1940 Weiner Filter (MIT) Feed Forward Networks
1962 Hough Transform Filter
1960 Extended Kalman Filter (NASA) 1965 Morphological Filters (ECOLE) Shared Weight Network (IBM)
Approaches Hidden Markov Model 1979 Alternating Sequential Filter (ERIM) Back Propagation Through Time
Gaussian Sum Filter Weighted Median Filter Self Organizing Maps
Condensation Filter (MS) Order Statistics Filters Dynamic Programming Networks
1993 Particle Filter Stack Filters 1997 Support Vector Machines (ATT)
Motivating Automatic Computer Data Analysis &
Applications Control Vision Financial Predictions
Non-Additive Measures,
Fundamental Non-Linear Integrals,
Theories and Random Sets
1954 Choquet Capacities/Integral (ADIF)
1975 Sugeno Measure/Integral (TIT)
Approaches Order Weighted Average
2000 Generalized Hidden Markov Model (UMC)
2003 Q-Filters (MOT)
2005 Q-Machines (MOT)
2009:01:24 Magdi A. Mohamed 3/23
4. Q-Metrics Modeling (QMM)
Computational Intelligence Applications & Impact on State of the Art
Supervised Learning Unsupervised Learning (NP-Hard)
Supervised Learning Objective : Unsupervised Learning Objective :
Find the set of centers, Q ⊂ P, that minimizes objective criterion
Find the form, f, that minimizes objective criterion
Φ(f) = ∑ distance ( f(p), t ) Ψ(Q) = ∑ min distance ( p, q )
q∈Q
p∈P p∈P
Applications Applications
• linear/nonlinear optimization • vector quantization & cluster analysis
• sequence analysis
• automatic feature extraction
• decision making
• visualization & dimensionality reduction
• compression (lossy & loss-free)
Impact on Existing Machine Learning Paradigms
1. Feed-Forward Artificial Neural Nets • automated data labeling & data cleaning tools
2. Genetic Computing • data mining & knowledge discovery
3. Tree Classifiers • continuous adaptation (automatic tuning &
4. Dynamic Programming & Reinforcement Learning customization)
5. Hidden Markov Models
Impact on Existing Machine Learning Paradigms
6. Nearest Prototype Classification
1. Crisp and Soft Clustering Algorithms
7. Crisp and Soft K-Nearest Neighbor Algorithms
2. Self Organizing Maps
8. Discriminant Analysis
3. Adaptive Resonance Theory
9. Support Vector Machines
2009:01:24 Magdi A. Mohamed 4/23
5. Support Vector Machines
Prior Art and Problem Statement
Linear Partitioning Nonlinear Partitioning Several Kernel Functions
• Original Theory developed by Vapnik & Chervonenkis (VC Dimension) in 1974
• Boser, Guyon & Vapnik (AT&T) issued first patent (US5649068(A)) in July 15, 1997
• Several Kernel functions K(x,x’) exist (linear and nonlinear)
• Kernel functions are defined using weighted Euclidean Distance (P-Metrics, P=2)
• Fixing P=2, and other parameters (such as γ) causes critical limitations
2009:01:24 Magdi A. Mohamed 5/23
6. The Idea
Systematic Application of Q-Metrics Modeling to Support Vector Machines
• A Q-Metric is defined for computing distances in a Q-Metric Based Support
Vector Machine (QMB-SVM) network using a variable parameter λ, that is
bounded between the real values -1 and 0 resulting in an efficient distance
function covering feasible range of potential metrics. The Q-Metric is
constructed by computing a polynomial in the variable parameter λ. The
parameter λ can then be automatically optimized to discover the ideal
functionalities of the Q-Metric, based on the data to be analyzed.
• The mathematical programming (training) task is formulated as an
optimization problem where the QMB-SVM network parameters are adjusted
to minimize an overall risk criterion quantified using Q-Metrics Modeling.
2009:01:24 Magdi A. Mohamed 6/23
7. Metrics
“distance indicators”
λ=-1
Max
P=INF
Euclidian
Q-Metrics P-Metrics P=2
Q-Metrics
QMB-SVM
Manhattan
Space
λ=0 P=1
Measures
Plausibility
ion
Believe Probability
λ=INF
ect
“confidence quantifiers”
ers
Int
ge
ge
Probability
era
Q-Aggregates
er a
λ=0
Av
Av
ion
Un
λ=-1
−1<λ<0 λ>0
λ=0
Aggregates Q-Measures
“connective operators”
2009:01:24 Magdi A. Mohamed 7/23
8. Implementation
Java Applet
2009:01:24 Magdi A. Mohamed 8/23
11. 4-Dimensional XOR Data Set
Nonlinear Classification Case
2009:01:24 Magdi A. Mohamed 11/23
12. More Experimental Results
Testing Kernel Types Using X-DATA Set
Type=0 Type=1 Type=2 Type=3 Type=4
B P/ B P/ B P/ B P/ B P/
B 21 15 B 36 00 B 36 00 B 07 29 B 36 00
P/ 21 15 P/ 13 23 P/ 08 28 P/ 27 09 P/ 00 36
Acc 50.0% Acc 81.9% Acc 88.9% Acc 22.2% Acc 100%
Conventional Conventional Conventional Conventional Novel
Linear Polynomial RBF Sigmoid QMB-RBF
2009:01:24 Magdi A. Mohamed 12/23
13. More Experimental Results
Testing Over Fitting Using X-DATA Set
Novel QMB-SVC
λ=-1.00 λ=-0.75 λ=-0.50 λ=-0.25 λ=0.00
Conventional RBF-SVC
γ=0.5 γ=11 γ=111 γ=1111 γ=11111
2009:01:24 Magdi A. Mohamed 13/23
14. Advantages of QMB-SVM
Characteristics and Promises
1. computational efficiency
2. numerical stability
3. per unit calculations simplify implementations (software and hardware)
4. suitability for massive parallel implementations
5. automatic discovery of multiple metric spaces
6. consistent handling of “curse of dimensionality” concerns
7. improvement over existing kernel functions
8. usability for both classification and regression applications
9. ease of use
“A hypothesis or theory is clear, decisive, and positive, but it is believed by no one
but the man who create it. Experimental findings, on the other hand, are messy,
inexact things, which are believed by everyone except the man who did the work.”
- Harlow Shapley
2009:01:24 Magdi A. Mohamed 14/23
15. Potential Applications
one vision for suites of techniques that work together
INPUTS
EVENTS
CLASSIFIER
SENSOR
SIGNALS
SIGNALS
ACTION CODES
FEATURES
SIGNAL DATA SIGNAL
SENSOR
SENSOR PRE- PROCESSING/ POST-
FUSION
PROCESSING ANALYSIS PROCESSING
ACTIONS
SENSOR
SIGNALS
ACTION CODES
DECISIONS
OUTPUTS
EVENTS
INPUTS
FEATURES
SIGNALS
SENSOR
CLASSIFIER
SIGNAL DATA SIGNAL
SENSOR CLASSIFER DECISION
SENSOR PRE- PROCESSING/ POST- DISPLAY
FUSION FUSION CONTROL
PROCESSING ANALYSIS PROCESSING
SENSOR
SIGNALS
SIGNALS
ACTION CODES
EVENTS
INPUTS
FEATURES
SENSOR CLASSIFIER
SIGNAL DATA SIGNAL
SENSOR
SENSOR PRE- PROCESSING/ POST-
FUSION
PROCESSING ANALYSIS PROCESSING
SENSOR
Q-AGGREGATES Q-FILTERS Q-METRICS Q-FILTERS Q-AGGREGATES
Q-METRICS
2009:01:24 Magdi A. Mohamed 15/23
16. Novel
QMB-SVC
2009:01:24 Magdi A. Mohamed 16/23
17. Conventional
RBF-SVC
2009:01:24 Magdi A. Mohamed 17/23
18. Novel
QMB-SVR
2009:01:24 Magdi A. Mohamed 18/23
19. Conventional
RBF-SVR
2009:01:24 Magdi A. Mohamed 19/23
20. Novel
QMB-SVC
2009:01:24 Magdi A. Mohamed 20/23
21. Conventional
RBF-SVC
2009:01:24 Magdi A. Mohamed 21/23
22. Novel
QMB-SVR
2009:01:24 Magdi A. Mohamed 22/23
23. Conventional
RBF-SVR
2009:01:24 Magdi A. Mohamed 23/23