Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
PR-252: Making Convolutional Networks Shift-Invariant AgainHyeongmin Lee
이번 논문은 Convolutional Neural Network에서 발생하는 Aliasing 문제를 지적하고, 이를 고전적인 신호처리 기법을 이용하여 해결하는 논문입니다.
Paper Link: https://arxiv.org/abs/1904.11486
Youtube Link: https://youtu.be/oTIBFH6M7YM
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
PR-252: Making Convolutional Networks Shift-Invariant AgainHyeongmin Lee
이번 논문은 Convolutional Neural Network에서 발생하는 Aliasing 문제를 지적하고, 이를 고전적인 신호처리 기법을 이용하여 해결하는 논문입니다.
Paper Link: https://arxiv.org/abs/1904.11486
Youtube Link: https://youtu.be/oTIBFH6M7YM
I am Arnold H. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, Nanyang Technological University. I have been helping students with their assignments for the past 10 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignment.
"Evaluation of the Hilbert Huang transformation of transient signals for brid...TRUSS ITN
Abstract: The assessment of bridge condition from vibration measurements has generally been determined via the monitoring of modal parameters determined though adaptations of the standard Fast Fourier Transform (FFT) or other stationary time-series based transformations. However, the non-stationary nature of measured vibration signals from damaged structures can limit the quality of frequency content information estimated by such methods. The Hilbert–Huang Transform’s (HHT) ability to decompose non-stationary measured vibration data into a time-frequency-energy representation allows signal variations to be identified sooner than other stationary-based transformations, thus potentially allowing early detection of damage. The present study uses data obtained from a progressive damage test conducted on a real bridge subjected to excitation from a double axle passing vehicle as a test subject. Decomposed vibration signals from the HHT and associated marginal spectrums are assessed to determine structural condition for various damage states and different locations along the bridge.
By Neil Roberts.
GPUs often provide half-float 16-bit registers for floating point calculations. Using these instead of full-precision 32-bit registers can often provide a significant performance benefit, particularly on embedded GPUs. The method used to expose these registers to applications in OpenGLES is that variables can be marked as mediump, meaning that the driver is allowed to use a lower precision for any operations involving these variables. The GLES spec allows for the lower precision to be optional so it is always valid to use a higher precision. Mesa currently implements the spec effectively by just ignoring the precision markers and always using full precision.
This talk will present ongoing work at Igalia to implement a lowering pass to convert mediump operations to 16-bit float operations. The work is targetting the Freedreno driver but the resulting lowering pass may be applicable to other drivers too.
(c) X.Org Developer's Conference (XDC) 2019
October 2-4 - Montréal, Canada
https://xdc2019.x.org/
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
I am Arnold H. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, Nanyang Technological University. I have been helping students with their assignments for the past 10 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignment.
"Evaluation of the Hilbert Huang transformation of transient signals for brid...TRUSS ITN
Abstract: The assessment of bridge condition from vibration measurements has generally been determined via the monitoring of modal parameters determined though adaptations of the standard Fast Fourier Transform (FFT) or other stationary time-series based transformations. However, the non-stationary nature of measured vibration signals from damaged structures can limit the quality of frequency content information estimated by such methods. The Hilbert–Huang Transform’s (HHT) ability to decompose non-stationary measured vibration data into a time-frequency-energy representation allows signal variations to be identified sooner than other stationary-based transformations, thus potentially allowing early detection of damage. The present study uses data obtained from a progressive damage test conducted on a real bridge subjected to excitation from a double axle passing vehicle as a test subject. Decomposed vibration signals from the HHT and associated marginal spectrums are assessed to determine structural condition for various damage states and different locations along the bridge.
By Neil Roberts.
GPUs often provide half-float 16-bit registers for floating point calculations. Using these instead of full-precision 32-bit registers can often provide a significant performance benefit, particularly on embedded GPUs. The method used to expose these registers to applications in OpenGLES is that variables can be marked as mediump, meaning that the driver is allowed to use a lower precision for any operations involving these variables. The GLES spec allows for the lower precision to be optional so it is always valid to use a higher precision. Mesa currently implements the spec effectively by just ignoring the precision markers and always using full precision.
This talk will present ongoing work at Igalia to implement a lowering pass to convert mediump operations to 16-bit float operations. The work is targetting the Freedreno driver but the resulting lowering pass may be applicable to other drivers too.
(c) X.Org Developer's Conference (XDC) 2019
October 2-4 - Montréal, Canada
https://xdc2019.x.org/
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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/
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
3. Previous Works
StyleGAN StyleGAN2 StyleGAN3
- Mapping network: input latent code z is transformed to
an intermediate latent code w.
- Affine transformation: makes w a style vector,
guaranteeing disentanglement of feature styles.
- Noise added to obtain stochastic variation on image.
4. Previous Works
StyleGAN StyleGAN2 StyleGAN3
- Blob shaped artifacts(water droplets) appeared in images
- Removed AdaIN and used weight demodulation to solve
the water droplet problem.
- Used a skip generator and residual discriminator instead
of PGGAN to produce high quality images
5. Previous Works
StyleGAN StyleGAN2 StyleGAN3
- Blob shaped artifacts(water droplets) appeared in images
- Removed AdaIN and used weight demodulation to solve
the water droplet problem.
- Used a skip generator and residual discriminator instead
of PGGAN to produce high quality images
12. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
13. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
14. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
15. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
16. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
17. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
Aliasing!!
18. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
19. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
20. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
21. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
22. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
Aliasing!!
23. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
24. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
25. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
26. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
27. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
Frequency Band
28. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
LPF
29. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
LPF
30. Aliasing? Fourier Transform? Bandlimit?
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
Frequency Band
Frequency Band
31. Conditions to prevent Aliasing
Condition 1).
Sampling process should accommodate appropriate sampling rate(frequency).
Condition 2).
Apply low-pass filter(LPF) to isolate unwanted high frequency components.
32. Where does aliasing occur ?
1. Upsampling Filters: - Non ideal filters
- Low pass filter not applied properly
- Unwanted high frequency components are
accumulated
2. Non-linearities such as ReLU: - Value sparks for negative values
33. How to solve Aliasing?
“ Our goal is to make every layer of G equivariant ~ “
34. How to solve Aliasing?
“ Our goal is to make every layer of G equivariant ~ “
35. How to solve Aliasing?
“ Our goal is to make every layer of G equivariant ~ “
Equivariance
= Change(variance) in the input is equally applied to the output
36. Four Operations on Two Transformations (Translation & Rotation)
1. Convolution
2. Up-sampling
3. Down-sampling
4. Non-Linearity
37. Four Operations on Two Transformations (Translation & Rotation)
1. Convolution
2. Up-sampling
3. Down-sampling
4. Non-Linearity
Condition 1).
Sampling process should accommodate appropriate sampling rate(frequency).
Condition 2).
Apply low-pass filter(LPF) to isolate unwanted high frequency components.
38. Discrete and Continuous Representation
Discretely sampled feature map
: Z
Discrete operation
: F
Discrete operation applied on feature map
: Z’ = F(Z)
39. Discrete and Continuous Representation
Discretely sampled feature map
: Z z
Discrete operation
: F f
Discrete operation applied on feature map
: Z’ = F(Z) z ’ = f(z)
40. Discrete and Continuous Representation
Discretely sampled feature map
: Z z
Discrete operation
: F f
Discrete operation applied on feature map
: Z’ = F(Z) z ’ = f(z)
Interpolation filter
Dirac comb function
