The document provides information on the current status of SPIRAL (System for Programming Integrated Algorithm Libraries). SPIRAL is a system developed by researchers at various universities to automatically generate highly optimized implementations of digital signal processing algorithms. It lists the main researchers and students involved in the project. It is supported by a research grant from DARPA. The document then provides background information on challenges in scientific computing due to Moore's Law and outlines SPIRAL's approach to address these challenges through automating implementation, optimization, and platform adaptation of DSP algorithms.
This summary provides the key details from the document in 3 sentences:
The document proposes a new method for encrypting two images into a single encrypted image using generalized weighted fractional Fourier transform (GWFRFT) with double random phase encoding. The encryption process involves applying pixel scrambling, phase encoding, and two rounds of GWFRFT with random phase masks on the combined image signal. This technique is shown to provide comparable security to the Advanced Encryption Standard (AES) with a 232-bit key size through a high number of possible permutations in the GWFRFT parameters and orders.
The document discusses frequency domain processing and the Fourier transform. It defines key concepts such as:
- The frequency domain represents how much of a signal lies within different frequency bands, while the time domain shows how a signal changes over time.
- The Fourier transform provides the frequency domain representation of a signal and is used to analyze signals with respect to frequency. Its inverse transform reconstructs the original signal.
- The Fourier transform decomposes a signal into orthogonal sine and cosine waves of different frequencies, showing the contribution of each frequency component. This representation is important for signal processing tasks like filtering.
The document discusses the Fourier transform and its applications in image processing. It begins with an introduction to the Fourier transform and its inventor. It then explains that the discrete Fourier transform (DFT) decomposes an image into sine and cosine components, representing the image in the frequency domain. The document provides details on how the DFT works, including using a fast Fourier transform to improve efficiency. It also describes how the Fourier transform output contains magnitude and phase information and discusses various applications of the Fourier transform in fields like signal and image processing.
The document discusses the Fast Fourier Transform (FFT) algorithm. It begins by explaining how the Discrete Fourier Transform (DFT) and its inverse can be computed on a digital computer, but require O(N2) operations for an N-point sequence. The FFT was discovered to reduce this complexity to O(NlogN) operations by exploiting redundancy in the DFT calculation. It achieves this through a recursive decomposition of the DFT into smaller DFT problems. The FFT provides a significant speedup and enables practical spectral analysis of long signals.
The document discusses the discrete Fourier transform (DFT) and its applications. It provides an overview of DFT and how it represents a signal in the frequency domain. It then describes the fast Fourier transform (FFT) algorithm, which efficiently computes the DFT. The document outlines algorithms to compute the inverse DFT and circular convolution using the DFT. It includes MATLAB code implementations of DFT, inverse DFT, FFT, and circular convolution. Graphs are shown comparing computation times of the algorithms.
This document discusses fast Fourier transform (FFT) algorithms. It provides an overview of FFTs and how they are more efficient than direct computation of the discrete Fourier transform (DFT). It describes decimation-in-time and decimation-in-frequency FFT algorithms and how they exploit properties of the DFT. The document also gives an example of calculating an 8-point DFT using the radix-2 decimation-in-frequency algorithm.
This document provides an overview of Fourier transforms and the fast Fourier transform (FFT) algorithm. It defines the continuous and discrete Fourier transforms, discusses their properties and examples. The FFT is introduced as an efficient algorithm for computing the discrete Fourier transform (DFT) in O(N log N) time rather than O(N2) time. The FFT decomposes the DFT calculation into butterfly operations between stages for inputs in bit-reversed order.
This summary provides the key details from the document in 3 sentences:
The document proposes a new method for encrypting two images into a single encrypted image using generalized weighted fractional Fourier transform (GWFRFT) with double random phase encoding. The encryption process involves applying pixel scrambling, phase encoding, and two rounds of GWFRFT with random phase masks on the combined image signal. This technique is shown to provide comparable security to the Advanced Encryption Standard (AES) with a 232-bit key size through a high number of possible permutations in the GWFRFT parameters and orders.
The document discusses frequency domain processing and the Fourier transform. It defines key concepts such as:
- The frequency domain represents how much of a signal lies within different frequency bands, while the time domain shows how a signal changes over time.
- The Fourier transform provides the frequency domain representation of a signal and is used to analyze signals with respect to frequency. Its inverse transform reconstructs the original signal.
- The Fourier transform decomposes a signal into orthogonal sine and cosine waves of different frequencies, showing the contribution of each frequency component. This representation is important for signal processing tasks like filtering.
The document discusses the Fourier transform and its applications in image processing. It begins with an introduction to the Fourier transform and its inventor. It then explains that the discrete Fourier transform (DFT) decomposes an image into sine and cosine components, representing the image in the frequency domain. The document provides details on how the DFT works, including using a fast Fourier transform to improve efficiency. It also describes how the Fourier transform output contains magnitude and phase information and discusses various applications of the Fourier transform in fields like signal and image processing.
The document discusses the Fast Fourier Transform (FFT) algorithm. It begins by explaining how the Discrete Fourier Transform (DFT) and its inverse can be computed on a digital computer, but require O(N2) operations for an N-point sequence. The FFT was discovered to reduce this complexity to O(NlogN) operations by exploiting redundancy in the DFT calculation. It achieves this through a recursive decomposition of the DFT into smaller DFT problems. The FFT provides a significant speedup and enables practical spectral analysis of long signals.
The document discusses the discrete Fourier transform (DFT) and its applications. It provides an overview of DFT and how it represents a signal in the frequency domain. It then describes the fast Fourier transform (FFT) algorithm, which efficiently computes the DFT. The document outlines algorithms to compute the inverse DFT and circular convolution using the DFT. It includes MATLAB code implementations of DFT, inverse DFT, FFT, and circular convolution. Graphs are shown comparing computation times of the algorithms.
This document discusses fast Fourier transform (FFT) algorithms. It provides an overview of FFTs and how they are more efficient than direct computation of the discrete Fourier transform (DFT). It describes decimation-in-time and decimation-in-frequency FFT algorithms and how they exploit properties of the DFT. The document also gives an example of calculating an 8-point DFT using the radix-2 decimation-in-frequency algorithm.
This document provides an overview of Fourier transforms and the fast Fourier transform (FFT) algorithm. It defines the continuous and discrete Fourier transforms, discusses their properties and examples. The FFT is introduced as an efficient algorithm for computing the discrete Fourier transform (DFT) in O(N log N) time rather than O(N2) time. The FFT decomposes the DFT calculation into butterfly operations between stages for inputs in bit-reversed order.
This document discusses the discrete Fourier transform (DFT) and fast Fourier transform (FFT). It begins by contrasting the frequency and time domains. It then defines the DFT, showing how it samples the discrete-time Fourier transform (DTFT) at discrete frequency points. It provides an example 4-point DFT calculation. It discusses the computational complexity of the direct DFT algorithm and how the FFT reduces this to O(N log N) by decomposing the DFT into smaller transforms. It explains the decimation-in-time FFT algorithm using butterfly operations across multiple stages. Finally, it notes that the inverse FFT can be computed using the FFT along with conjugation and scaling steps.
This document discusses various unitary transforms that can be used to decompose images, including the discrete Fourier transform (DFT), discrete cosine transform (DCT), Karhunen-Loève transform (KLT), Hadamard transform, and wavelet transforms. Unitary transforms have desirable properties like energy conservation, orthonormal bases, and de-correlation of image elements. The KLT provides optimal energy compaction and de-correlation but relies on signal statistics. Practical transforms like the DCT approximate the KLT while having fast implementations and being signal-independent. Transforms are widely used for applications like image compression, feature extraction, and pattern recognition.
This document provides an overview of frequency analysis techniques for signals and systems, including the Fourier series, Fourier transform, discrete-time Fourier series (DTFS), discrete-time Fourier transform (DTFT), and discrete Fourier transform (DFT). It discusses properties and applications of these techniques, such as analyzing periodic and aperiodic signals. Examples are provided to illustrate calculating the Fourier series and transform of simple signals. The document also covers sampling theory and the Nyquist criterion for proper reconstruction of signals from samples.
The document summarizes key concepts about the Fast Fourier Transform (FFT). The FFT converts discrete time domain data into its frequency spectrum components. It breaks down a signal into its constituent frequencies. The document explains key FFT terms like sampling rate, fundamental period, Nyquist frequency, and Euler's formula. It then works through an example of applying the FFT to a sampled sine wave to demonstrate how it extracts the wave's 10Hz frequency component.
This project report describes the implementation of the Fast Fourier Transform (FFT) algorithm using LabVIEW. The FFT is an optimized version of the Discrete Fourier Transform (DFT) that reduces redundant calculations, making it faster. The report defines the FFT and DFT, describes the FFT algorithm including twiddle factors and a 3-stage radix-2 approach. It discusses how FFT is applied using a divide and conquer method. The LabVIEW block diagram and front panel for input/output are shown. Applications of FFT include spectral analysis, digital filtering, medical imaging, and instrumentation.
This document summarizes key aspects of the discrete Fourier transform (DFT). It defines the DFT, provides the formula for calculating it, and explains that the DFT transforms a discrete-time signal from the time domain to the frequency domain. It also outlines several important properties of the DFT, including linearity, shift property, duality, symmetry, and circular convolution. Examples are provided to illustrate duality and symmetry. References for further information on the discrete Fourier transform are also included.
This document discusses frequency domain processing and various image transforms, with a focus on the discrete Fourier transform (DFT). It provides definitions and properties of the DFT, including its relationship to the Fourier transform and examples of applying the DFT to images. Other transforms discussed include the Walsh transform, with examples provided of computing and displaying the Walsh transform of an image. MATLAB code is presented for calculating the DFT and Walsh transform of grayscale images.
The document discusses the Fast Fourier Transform (FFT) algorithm.
1) The FFT is a set of techniques that exploits symmetries in the Discrete Fourier Transform (DFT) to make its computation much faster. The speedup increases with larger DFT sizes.
2) The Cooley-Tukey algorithm decomposes an N-point DFT into smaller DFTs by splitting the indices, resulting in an algorithm that is proportional to NlogN operations rather than N^2.
3) The algorithm can be represented as a series of "butterfly" operations, with each butterfly requiring only 2 multiplications. This reduces the number of multiplications needed compared to direct computation of the DFT.
The document discusses the Discrete Fourier Transform (DFT). It explains that the DFT represents a finite-length sequence by the samples of its Discrete-Time Fourier Transform (DTFT). These samples are called the DFT coefficients of the sequence. The DFT provides a transformation between the time and frequency domains. It has various properties like linearity, duality, and relationships between shifting sequences and their DFTs. Circular convolution in the time domain can be computed as multiplication of DFT coefficients in the frequency domain. Examples are provided to illustrate these concepts.
DSP_FOEHU - Lec 09 - Fast Fourier TransformAmr E. Mohamed
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that the FFT decomposes an N-point discrete Fourier transform (DFT) into smaller DFTs of size N/2, taking advantage of the periodicity and symmetry of complex numbers. For N that is a power of 2, it separates the input sequence into even and odd indexed parts, then recursively applies this decomposition until 2-point DFTs are reached. This decimation-in-time approach reduces the computational complexity from O(N^2) for the direct DFT to O(NlogN) for the FFT.
Signal Processing Introduction using Fourier TransformsArvind Devaraj
1) The document introduces signal processing by discussing signals, systems, and transforms. It defines signals as functions of time or space and systems as maps that manipulate signals. Transforms represent signals in different domains like frequency to simplify operations.
2) Signals can be represented in the frequency domain using Fourier transforms. This makes operations like filtering easier. Low frequencies represent overall shape while high frequencies are details like noise or edges.
3) Linear and time-invariant systems can be characterized by their impulse response. The output is the convolution of the input and impulse response. Convolution is a mechanism that shapes signals to produce outputs.
This document discusses the discrete-time Fourier transform (DTFT). It begins by introducing the DTFT and how it can be used to represent aperiodic signals as the sum of complex exponentials. Several properties of the DTFT are then discussed, including linearity, time/frequency shifting, periodicity, and conjugate symmetry. Examples are provided to illustrate how to compute the DTFT of simple signals. The document also discusses how the DTFT can be used to represent periodic signals and impulse trains.
DSP_FOEHU - Lec 08 - The Discrete Fourier TransformAmr E. Mohamed
The document discusses the Discrete Fourier Transform (DFT). It explains that while the discrete-time Fourier transform (DTFT) and z-transform are not numerically computable, the DFT avoids this issue. The DFT represents periodic sequences as a sum of complex exponentials with frequencies that are integer multiples of the fundamental frequency. It can be viewed as computing samples of the DTFT or z-transform at discrete frequency points, allowing numerical computation. The DFT provides a link between the time and frequency domain representations of a finite-length sequence.
NIPS2017 Few-shot Learning and Graph ConvolutionKazuki Fujikawa
The document discusses meta-learning and prototypical networks for few-shot learning. It introduces prototypical networks, which learn a metric space such that classification can be performed by finding the nearest class prototype to a query example in embedding space. The document summarizes results on few-shot image classification benchmarks like Omniglot and miniImageNet, finding that prototypical networks achieve state-of-the-art performance.
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that FFT reduces the number of computations needed to calculate the Discrete Fourier Transform (DFT) of a sequence by decomposing the DFT into successive DFTs of smaller sizes. Specifically, it breaks down the N point DFT into multiple N/2 point DFTs recursively until it reaches DFTs of size 1. This decomposition reduces the complexity from O(N^2) for DFT to O(NlogN) for FFT.
This document contains lecture notes on sparse autoencoders. It begins with an introduction describing the limitations of supervised learning and the need for algorithms that can automatically learn feature representations from unlabeled data. The notes then state that sparse autoencoders are one approach to learn features from unlabeled data, and describe the organization of the rest of the notes. The notes will cover feedforward neural networks, backpropagation for supervised learning, autoencoders for unsupervised learning, and how sparse autoencoders are derived from these concepts.
This document presents an implementation of the Fast Fourier Transform (FFT) algorithm. It begins with an introduction to FFTs, explaining that they can compute the Discrete Fourier Transform (DFT) much more efficiently than direct evaluation, reducing the computation time from O(N^2) to O(N log N). It then describes the basic butterfly structures used in FFTs and shows how to implement 16-point FFT blocks. The document includes MATLAB code for an 8-point DFT and FFT, as well as VHDL code for a 16-point FFT processor. It provides details on decimation-in-time and decimation-in-frequency algorithms and how they recursively break down the DFT into smaller transforms
The Fourier transform decomposes a signal into its constituent frequencies, representing it in the frequency domain rather than the spatial domain, which can make certain operations and analyses easier to perform; it has both magnitude and phase components that provide information about the frequency content and relative phases of the signal. The discrete Fourier transform (DFT) is a sampled version of the continuous Fourier transform that is useful for digital signal and image processing applications.
This document provides tips for small businesses on marketing their products on social media. It begins by defining social media as websites based on user participation and user-generated content. It then discusses the implications of social media for businesses, customers, and society. The document provides seven tips for social media marketing, including sharing engaging content, posting at peak traffic times, asking open-ended questions, using social media for customer support, choosing the right platform, focusing efforts, and measuring performance. It also covers some legal issues to consider with social media advertising.
This document summarizes a presentation on intellectual property given on October 6, 2015 at Fairfield University by Jonathan Winter. It discusses the main types of intellectual property including patents, trademarks, copyrights, and trade secrets. For each type, it provides details on what is protected, requirements for protection, benefits of registration or protection, infringement issues, and costs. It focuses particularly on copyright, trademarks, and patents, providing examples for each. The overall presentation aims to educate startups on intellectual property and when they should consider pursuing protection.
This document discusses the discrete Fourier transform (DFT) and fast Fourier transform (FFT). It begins by contrasting the frequency and time domains. It then defines the DFT, showing how it samples the discrete-time Fourier transform (DTFT) at discrete frequency points. It provides an example 4-point DFT calculation. It discusses the computational complexity of the direct DFT algorithm and how the FFT reduces this to O(N log N) by decomposing the DFT into smaller transforms. It explains the decimation-in-time FFT algorithm using butterfly operations across multiple stages. Finally, it notes that the inverse FFT can be computed using the FFT along with conjugation and scaling steps.
This document discusses various unitary transforms that can be used to decompose images, including the discrete Fourier transform (DFT), discrete cosine transform (DCT), Karhunen-Loève transform (KLT), Hadamard transform, and wavelet transforms. Unitary transforms have desirable properties like energy conservation, orthonormal bases, and de-correlation of image elements. The KLT provides optimal energy compaction and de-correlation but relies on signal statistics. Practical transforms like the DCT approximate the KLT while having fast implementations and being signal-independent. Transforms are widely used for applications like image compression, feature extraction, and pattern recognition.
This document provides an overview of frequency analysis techniques for signals and systems, including the Fourier series, Fourier transform, discrete-time Fourier series (DTFS), discrete-time Fourier transform (DTFT), and discrete Fourier transform (DFT). It discusses properties and applications of these techniques, such as analyzing periodic and aperiodic signals. Examples are provided to illustrate calculating the Fourier series and transform of simple signals. The document also covers sampling theory and the Nyquist criterion for proper reconstruction of signals from samples.
The document summarizes key concepts about the Fast Fourier Transform (FFT). The FFT converts discrete time domain data into its frequency spectrum components. It breaks down a signal into its constituent frequencies. The document explains key FFT terms like sampling rate, fundamental period, Nyquist frequency, and Euler's formula. It then works through an example of applying the FFT to a sampled sine wave to demonstrate how it extracts the wave's 10Hz frequency component.
This project report describes the implementation of the Fast Fourier Transform (FFT) algorithm using LabVIEW. The FFT is an optimized version of the Discrete Fourier Transform (DFT) that reduces redundant calculations, making it faster. The report defines the FFT and DFT, describes the FFT algorithm including twiddle factors and a 3-stage radix-2 approach. It discusses how FFT is applied using a divide and conquer method. The LabVIEW block diagram and front panel for input/output are shown. Applications of FFT include spectral analysis, digital filtering, medical imaging, and instrumentation.
This document summarizes key aspects of the discrete Fourier transform (DFT). It defines the DFT, provides the formula for calculating it, and explains that the DFT transforms a discrete-time signal from the time domain to the frequency domain. It also outlines several important properties of the DFT, including linearity, shift property, duality, symmetry, and circular convolution. Examples are provided to illustrate duality and symmetry. References for further information on the discrete Fourier transform are also included.
This document discusses frequency domain processing and various image transforms, with a focus on the discrete Fourier transform (DFT). It provides definitions and properties of the DFT, including its relationship to the Fourier transform and examples of applying the DFT to images. Other transforms discussed include the Walsh transform, with examples provided of computing and displaying the Walsh transform of an image. MATLAB code is presented for calculating the DFT and Walsh transform of grayscale images.
The document discusses the Fast Fourier Transform (FFT) algorithm.
1) The FFT is a set of techniques that exploits symmetries in the Discrete Fourier Transform (DFT) to make its computation much faster. The speedup increases with larger DFT sizes.
2) The Cooley-Tukey algorithm decomposes an N-point DFT into smaller DFTs by splitting the indices, resulting in an algorithm that is proportional to NlogN operations rather than N^2.
3) The algorithm can be represented as a series of "butterfly" operations, with each butterfly requiring only 2 multiplications. This reduces the number of multiplications needed compared to direct computation of the DFT.
The document discusses the Discrete Fourier Transform (DFT). It explains that the DFT represents a finite-length sequence by the samples of its Discrete-Time Fourier Transform (DTFT). These samples are called the DFT coefficients of the sequence. The DFT provides a transformation between the time and frequency domains. It has various properties like linearity, duality, and relationships between shifting sequences and their DFTs. Circular convolution in the time domain can be computed as multiplication of DFT coefficients in the frequency domain. Examples are provided to illustrate these concepts.
DSP_FOEHU - Lec 09 - Fast Fourier TransformAmr E. Mohamed
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that the FFT decomposes an N-point discrete Fourier transform (DFT) into smaller DFTs of size N/2, taking advantage of the periodicity and symmetry of complex numbers. For N that is a power of 2, it separates the input sequence into even and odd indexed parts, then recursively applies this decomposition until 2-point DFTs are reached. This decimation-in-time approach reduces the computational complexity from O(N^2) for the direct DFT to O(NlogN) for the FFT.
Signal Processing Introduction using Fourier TransformsArvind Devaraj
1) The document introduces signal processing by discussing signals, systems, and transforms. It defines signals as functions of time or space and systems as maps that manipulate signals. Transforms represent signals in different domains like frequency to simplify operations.
2) Signals can be represented in the frequency domain using Fourier transforms. This makes operations like filtering easier. Low frequencies represent overall shape while high frequencies are details like noise or edges.
3) Linear and time-invariant systems can be characterized by their impulse response. The output is the convolution of the input and impulse response. Convolution is a mechanism that shapes signals to produce outputs.
This document discusses the discrete-time Fourier transform (DTFT). It begins by introducing the DTFT and how it can be used to represent aperiodic signals as the sum of complex exponentials. Several properties of the DTFT are then discussed, including linearity, time/frequency shifting, periodicity, and conjugate symmetry. Examples are provided to illustrate how to compute the DTFT of simple signals. The document also discusses how the DTFT can be used to represent periodic signals and impulse trains.
DSP_FOEHU - Lec 08 - The Discrete Fourier TransformAmr E. Mohamed
The document discusses the Discrete Fourier Transform (DFT). It explains that while the discrete-time Fourier transform (DTFT) and z-transform are not numerically computable, the DFT avoids this issue. The DFT represents periodic sequences as a sum of complex exponentials with frequencies that are integer multiples of the fundamental frequency. It can be viewed as computing samples of the DTFT or z-transform at discrete frequency points, allowing numerical computation. The DFT provides a link between the time and frequency domain representations of a finite-length sequence.
NIPS2017 Few-shot Learning and Graph ConvolutionKazuki Fujikawa
The document discusses meta-learning and prototypical networks for few-shot learning. It introduces prototypical networks, which learn a metric space such that classification can be performed by finding the nearest class prototype to a query example in embedding space. The document summarizes results on few-shot image classification benchmarks like Omniglot and miniImageNet, finding that prototypical networks achieve state-of-the-art performance.
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that FFT reduces the number of computations needed to calculate the Discrete Fourier Transform (DFT) of a sequence by decomposing the DFT into successive DFTs of smaller sizes. Specifically, it breaks down the N point DFT into multiple N/2 point DFTs recursively until it reaches DFTs of size 1. This decomposition reduces the complexity from O(N^2) for DFT to O(NlogN) for FFT.
This document contains lecture notes on sparse autoencoders. It begins with an introduction describing the limitations of supervised learning and the need for algorithms that can automatically learn feature representations from unlabeled data. The notes then state that sparse autoencoders are one approach to learn features from unlabeled data, and describe the organization of the rest of the notes. The notes will cover feedforward neural networks, backpropagation for supervised learning, autoencoders for unsupervised learning, and how sparse autoencoders are derived from these concepts.
This document presents an implementation of the Fast Fourier Transform (FFT) algorithm. It begins with an introduction to FFTs, explaining that they can compute the Discrete Fourier Transform (DFT) much more efficiently than direct evaluation, reducing the computation time from O(N^2) to O(N log N). It then describes the basic butterfly structures used in FFTs and shows how to implement 16-point FFT blocks. The document includes MATLAB code for an 8-point DFT and FFT, as well as VHDL code for a 16-point FFT processor. It provides details on decimation-in-time and decimation-in-frequency algorithms and how they recursively break down the DFT into smaller transforms
The Fourier transform decomposes a signal into its constituent frequencies, representing it in the frequency domain rather than the spatial domain, which can make certain operations and analyses easier to perform; it has both magnitude and phase components that provide information about the frequency content and relative phases of the signal. The discrete Fourier transform (DFT) is a sampled version of the continuous Fourier transform that is useful for digital signal and image processing applications.
This document provides tips for small businesses on marketing their products on social media. It begins by defining social media as websites based on user participation and user-generated content. It then discusses the implications of social media for businesses, customers, and society. The document provides seven tips for social media marketing, including sharing engaging content, posting at peak traffic times, asking open-ended questions, using social media for customer support, choosing the right platform, focusing efforts, and measuring performance. It also covers some legal issues to consider with social media advertising.
This document summarizes a presentation on intellectual property given on October 6, 2015 at Fairfield University by Jonathan Winter. It discusses the main types of intellectual property including patents, trademarks, copyrights, and trade secrets. For each type, it provides details on what is protected, requirements for protection, benefits of registration or protection, infringement issues, and costs. It focuses particularly on copyright, trademarks, and patents, providing examples for each. The overall presentation aims to educate startups on intellectual property and when they should consider pursuing protection.
The document discusses several key functions of link layer protocols:
1) Framing and link access to encapsulate data into frames using physical addresses and implement channel access on shared mediums.
2) Reliable delivery techniques like error detection, error correction, and flow control to ensure reliable transmission.
3) Common error detection techniques used in link layers include parity checks and cyclic redundancy checks (CRCs) which use checksums to detect errors. CRCs can detect all single and double bit errors and most burst errors.
The document discusses image processing in the frequency domain. It covers topics such as the Fourier transform, Hartley transform, and their fast variants. The Fourier transform represents an image as a sum of sine and cosine waves, while the Hartley transform uses only real numbers. Convolution can be performed efficiently in the frequency domain by multiplying the Fourier/Hartley transforms of the image and template and taking the inverse transform. Deconvolution involves finding the inverse of the template to recover the original image from its convolution with the template.
This document provides information about the Intellectual Property and Entrepreneurship Law Clinic at the University of Connecticut School of Law. It introduces the supervising attorneys and describes the types of intellectual property legal services provided by law students under attorney supervision. The Clinic accepts applications for legal assistance from individuals and businesses in Connecticut meeting certain criteria, but acceptance is not guaranteed and does not create an attorney-client relationship. Review of applications and decisions may take several weeks.
2010 Leveraging technology for content deliveryWCET
The International Hispanic Online University (IHOU) provides core classes in Spanish to Hispanic students to increase degree attainment. IHOU leverages technology like the BrainHoney learning management system and Genius student information system for content delivery. IHOU conducted a pilot of general education courses developed through a collaborative process to identify lessons learned for improving the virtual instructional design and student experience.
2011Challenges and Successess in Faculty DevelopmentWCET
This document discusses challenges and strategies for faculty development at different types of institutions. It profiles three institutions: Grand Canyon University (for-profit), Park University (private non-profit), and Boise State University (public). All three institutions face challenges related to communication, resources, and motivating adjunct and full-time faculty. However, they employ different strategies like online portals, communities of practice, and stipends to better support faculty and scale development programs. Key themes are creating community, effective communication, pedagogical focus, and flexible faculty development models.
This document summarizes a survey on optimizing faculty workload and learning effectiveness. The survey was conducted between 2008-2012 with 29 institutions responding. Preliminary findings show a wide range in faculty teaching loads, from 9-72 credit hours per year for full-time faculty and 9-60 hours for adjuncts. Factors like class size, preparation time, and communication time vary significantly. Respondents identified the number of classes taught and administrative duties as most impacting workload. The document discusses further analyzing the findings and continuing the national dialogue on relating workload to student outcomes.
Educational engineers design effective and dynamic learning environments by connecting educational theory and research to tools and materials. They determine educational goals and design spaces to meet those goals, using data to assess outcomes. Unlike traditional teachers focused on standardization, educational engineers create environments where students can access personalized learning to address their immediate needs. The role requires precision in planning, understanding how to rethink learning spaces, materials, tools, learners and assessment.
2010 Are They Students? Or Customers? Does it Matter?WCET
Contact North/elearnnetwork.ca is Canada's largest distance education network spanning Ontario with 112 local centers. They were previously frustrated with outsourcing their customer relationship management (CRM) software and tracking of student progress. They decided to build their own custom-made in-house CRM to maintain control over critical tools and allow customization. While insourcing required programmers and quality control, they were still able to save money compared to outsourcing, though scope creep was a challenge that required strict project management and user testing. In the end, focusing on supporting students helped them overcome difficulties in developing their new in-house CRM system.
2010 Does Social Networking Equate to Social Learning?WCET
The document discusses key issues around using social media for learning, including the cognitive effects of social media on learning, implications of different social media tools, and challenges of creating educational applications. It raises questions about defining social learning, the impact of online identities, facilitating learning outside the classroom, ensuring deep learning and reflection occurs, providing appropriate professional development for teachers, and the role of instructors within social media venues.
This document provides information about distance education and training programs offered through an Ontario-based non-profit organization. It operates 112 access centers across Ontario that provide students with online courses, program information, and application assistance from 25 colleges and 21 universities. The access centers offer orientation, technical support, and encouragement to students pursuing distance education courses and programs.
This document summarizes a presentation on videoconferencing given at the WCET Annual Meeting in San Antonio, TX on November 1, 2012. It discusses how Utah State University and the University of Utah College of Nursing use interactive videoconferencing (IVC) to deliver distance education programs. IVC allows them to reach students across multiple campuses. The University of Utah specifically uses IVC to deliver its PhD nursing program entirely at a distance. Both institutions discuss best practices for IVC classroom design and challenges with finding an affordable single solution for high-quality synchronous video and web conferencing.
Supplemental instruction (SI) provides peer-led study sessions to support students in traditionally difficult courses. SI was developed at the University of Missouri in 1983 and is now used by over 800 institutions worldwide. SI sessions are facilitated by student leaders who attend regular course meetings and engage with course materials. Leaders guide interactive study sessions outside of class without the instructor present. The University of Wyoming implements online SI to support students in outreach programs. Peer leaders are trained to lead virtual study sessions synchronously using web conferencing tools. Student feedback suggests online SI is an effective way to get questions answered and better understand course content and expectations.
This document provides information about a workshop on creating viral videos using iPad, iMovie, and Web 2.0 tools. The workshop will take place on October 27, 2011 from 3:15-4:15 PM in the Pomeroy room. It includes links to YouTube's most viewed videos feed and an arc map of internet traffic created by Stephen Eick.
Instructional technologies have evolved over time but some criticisms remain the same. Early books were criticized for making students seem knowledgeable without truly teaching them. Modern computers in 1987 offered processing power and storage that seem limited now but were state of the art then. While the specific technologies change, concerns about surface-level learning versus deep understanding continue.
The document describes a study conducted using a remote proctoring system called Remote Proctor Pro to monitor online students taking exams, finding that it effectively ensured academic integrity while being more convenient and affordable than in-person proctoring options. The system authenticates student identity, monitors their activity, secures their computer access, records exam sessions, and reviews for any violations of exam policies.
The document appears to be diary entries from a teacher over 20 days as they try to incorporate video technology into their teaching to better connect with students. The teacher initially thinks video will help with connection but finds it is more difficult than expected as they deal with administrative issues, technical problems, and struggle to choose the right options and tools for their needs and classes.
This document provides an outline for a textbook on advanced digital signal processing. It covers topics such as sampling theory, the discrete Fourier transform and fast Fourier transform, digital filters, multirate digital signal processing, and spectral estimation. Sampling theory concepts discussed include the sampling theorem, which states that a continuous-time signal can be reconstructed from its samples if it is bandlimited and sampled at a rate at least twice its highest frequency. Non-ideal effects of sampling such as aliasing are also covered.
This document provides an outline and introduction to the key concepts in advanced digital signal processing. It discusses sampling of continuous-time signals to generate discrete-time signals, including the sampling theorem which establishes conditions for perfect reconstruction of a signal from its samples. It also introduces digital signal processors and discusses common operations in digital signal processing like the discrete Fourier transform.
The document provides an overview of Fourier analysis and its applications in image processing. It discusses the history and development of Fourier analysis. Key concepts covered include periodic signals, Fourier series, the Fourier transform, discrete Fourier transform (DFT), and fast Fourier transform (FFT). It also describes how the 2D FFT and DFT can be applied to digital images for tasks like spatial frequency analysis and image filtering.
Ajay Kumar.Ph.D Research scholar at National Institute of Technology my mail id:-- ajaymodaliger@gmail.com
In this presentation slide i have Explained how to reducing Computational time complexity of Discrete Fourier transform(DFT) from O(n^2 ) to nlogn through Radix-2 .FFT Algorithm in this work i have also introduced how we can use Radix-2 FFT in encrypted signal processing application by considering homomarphic properties(RSA) of Paillier cryptosystem.
Low rank tensor approximation of probability density and characteristic funct...Alexander Litvinenko
This document summarizes a presentation on computing divergences and distances between high-dimensional probability density functions (pdfs) represented using tensor formats. It discusses:
1) Motivating the problem using examples from stochastic PDEs and functional representations of uncertainties.
2) Computing Kullback-Leibler divergence and other divergences when pdfs are not directly available.
3) Representing probability characteristic functions and approximating pdfs using tensor decompositions like CP and TT formats.
4) Numerical examples computing Kullback-Leibler divergence and Hellinger distance between Gaussian and alpha-stable distributions using these tensor approximations.
This document contains a model examination for a Digital Signal Processing course. It includes two parts - Part A contains 10 short answer questions and Part B contains 5 long answer questions worth 16 marks each. The questions cover various topics in DSP including linear, time-invariant systems, Fourier transforms, FIR and IIR filter design, multirate signal processing, and speech processing applications. Students are required to answer 10 out of 15 questions in total within the 3 hour examination duration.
Sampling and Reconstruction (Online Learning).pptxHamzaJaved306957
1. Sampling and reconstruction of signals was analyzed using the impulse sampling math model.
2. The analysis showed that a bandlimited signal can be perfectly reconstructed from its samples as long as the sampling rate is at least twice the bandwidth of the signal.
3. If the sampling rate is lower than the minimum required rate, aliasing error occurs where frequency components fold back into the baseband.
FourierTransform detailed power point presentationssuseracb8ba
The document discusses the Fourier transform and its applications in image processing. Some key points:
- The Fourier transform decomposes a function into its constituent frequencies, allowing operations to be performed in the frequency domain. It has inverses that convert back to the spatial domain.
- Common transforms include the discrete Fourier transform (DFT) which samples a continuous function, and the discrete time Fourier transform (DTFT) which is periodic.
- The Fourier transform is useful for image processing tasks like frequency-domain filtering to remove undesirable frequencies like noise or blur. It also speeds up operations like convolution.
- Low frequencies in images correspond to smooth areas while high frequencies correspond to edges. Removing high frequencies results in a
The document summarizes a presentation on revocable identity-based encryption (RIBE) from codes with rank metric. Key points:
- RIBE adds an efficient revocation procedure to identity-based encryption by using a binary tree structure and key updates.
- The construction is based on low rank parity-check codes, with the master secret key defined as the "trapdoor" generated by the RankSign algorithm.
- Security relies on the rank syndrome decoding problem. Key updates are done efficiently through the binary tree with logarithmic complexity.
- Parameters are given that allow decoding of up to 2wr errors with small failure probability, suitable for the identity-based encryption scheme.
This document provides information about an expert systems and solutions company located in Paiyanoor, Chennai that works with students on research projects. The company has labs where students can assemble hardware and receive guidance from experts. They are looking for final year students and Ph.D students from electrical and electronics fields to work on projects.
This document provides an overview of the Fourier transform and discrete Fourier transform (DFT). It introduces complex numbers and periodic sine and cosine functions used as the basis for Fourier analysis. The Fourier transform decomposes a signal into its frequency components, and the DFT does the same for discrete, finite signals. Key properties of the DFT include separability, periodicity, translation, rotation, and how operations in the spatial and frequency domains relate. The magnitude and phase components of the DFT encode important information about the original signal.
This document describes a course on digital signal processor architecture. It includes the course objectives, outcomes, contents, and references. The course objectives are to focus on architectural requirements, concepts, programming, and interfacing of digital signal processors. The outcomes are for students to describe DSP basics, architectures, instructions, programming, and interfacing memory and I/O peripherals. The contents cover topics such as DSP systems, computational accuracy, architectures, programming, algorithms, and interfacing. References for textbooks on DSP processors and algorithms are also provided.
The document discusses digital image processing and two-dimensional transforms. It provides an agenda that covers two-dimensional mathematical preliminaries and two transforms: the discrete Fourier transform (DFT) and discrete cosine transform (DCT). It then discusses the DFT and DCT in more detail over several pages, covering properties, examples, and applications such as image compression.
Direct split-radix algorithm for fast computation of type-II discrete Hartley...TELKOMNIKA JOURNAL
In this paper, a novel split-radix algorithm for fast calculation the discrete Hartley transform of type-II (DHT-II) is intoduced. The algorithm is established through the decimation in time (DIT) approach, and implementedby splitting a length N of DHT-II into one DHT-II of length N/2 for even-indexed samples and two DHTs-II of length N/4 for odd-indexed samples. The proposed algorithm possesses the desired properties such as regularity, inplace calculation and it is represented by simple closed form decomposition sleading to considerable reductions in the arithmetic complexity compared to the existing DHT-II algorithms. Additionally, the validity of the proposed algorithm has been confirmed through analysing the arithmetic complexityby calculating the number of real additions and multiplications and associating it with the existing DHT-II algorithms.
The document proposes an efficient combined single-path delay commutator and multi-path delay feedback (SDC-SDF) radix-4 pipelined fast Fourier transform (FFT) architecture. The architecture includes SDC stages and one SDF stage. The SDC processing engine achieves 100% hardware utilization by time-multiplexing arithmetic resources including adders and multipliers. The proposed architecture requires roughly a minimum number of complex adders and delay memory of 4N+3.0. It provides a concise output order from the pre-stage to stage N/4-1 of a 16 point FFT example.
s.Magesh kumar DECE,BTECH,ME (ASAN MEMORIAL COLLEGE OF ENGINEERING AND TECHNO...sakthi1986
The document proposes a combined single-path delay commutator and multi-path delay feedback (SDC-SDF) radix-4 pipelined fast Fourier transform architecture. The architecture includes SDC stages and one SDF stage. The SDC processing engine achieves 100% hardware resource utilization by time-multiplexing arithmetic resources like adders and multipliers. The proposed architecture requires roughly a minimum number of complex adders and delay memory of 4N+3. It provides high throughput and low latency for applications like OFDM while improving hardware utilization compared to other radix-4 SDC-SDF architectures.
Performance evaluations of grioryan fft and cooley tukey fft onto xilinx virt...csandit
A large family of signal processing techniques consist of Fourier-transforming a signal,
manipulating the Fourier-transformed data in a simple way, and reversing the transformation.
We widely use Fourier frequency analysis in equalization of audio recordings, X-ray
crystallography, artefact removal in Neurological signal and image processing, Voice Activity
Detection in Brain stem speech evoked potentials, speech processing spectrograms are used to
identify phonetic sounds and so on. Discrete Fourier Transform (DFT) is a principal
mathematical method for the frequency analysis. The way of splitting the DFT gives out various
fast algorithms. In this paper, we present the implementation of two fast algorithms for the DFT
for evaluating their performance. One of them is the popular radix-2 Cooley-Tukey fast Fourier
transform algorithm (FFT) [1] and the other one is the Grigoryan FFT based on the splitting by
the paired transform [2]. We evaluate the performance of these algorithms by implementing
them on the Xilinx Virtex-II pro [3] and Virtex-5 [4] FPGAs, by developing our own FFT
processor architectures. Finally we show that the Grigoryan FFT is working fatser than
Cooley-Tukey FFT, consequently it is useful for higher sampling rates. Operating at higher
sampling rates is a challenge in DSP applications.
PERFORMANCE EVALUATIONS OF GRIORYAN FFT AND COOLEY-TUKEY FFT ONTO XILINX VIRT...cscpconf
A large family of signal processing techniques consist of Fourier-transforming a signal,manipulating the Fourier-transformed data in a simple way, and reversing the transformation.We widely use Fourier frequency analysis in equalization of audio recordings, X-ray crystallography, artefact removal in Neurological signal and image processing, Voice Activity Detection in Brain stem speech evoked potentials, speech processing spectrograms are used to identify phonetic sounds and so on. Discrete Fourier Transform (DFT) is a principal mathematical method for the frequency analysis. The way of splitting the DFT gives out various fast algorithms. In this paper, we present the implementation of two fast algorithms for the DFT for evaluating their performance. One of them is the popular radix-2 Cooley-Tukey fast Fourier transform algorithm (FFT) [1] and the other one is the Grigoryan FFT based on the splitting by the paired transform [2]. We evaluate the performance of these algorithms by implementing
them on the Xilinx Virtex-II pro [3] and Virtex-5 [4] FPGAs, by developing our own FFT processor architectures. Finally we show that the Grigoryan FFT is working fatser than
Cooley-Tukey FFT, consequently it is useful for higher sampling rates. Operating at higher
sampling rates is a challenge in DSP applications
Implementation Of Grigoryan FFT For Its Performance Case Study Over Cooley-Tu...ijma
This document discusses the implementation and performance comparison of two fast Fourier transform (FFT) algorithms - the Cooley-Tukey FFT and the Grigoryan FFT - on three Xilinx FPGAs. The Grigoryan FFT uses a decomposition based on paired transforms, while the Cooley-Tukey FFT uses a radix-2 decomposition. Both algorithms were implemented on Virtex-II Pro, Virtex-5, and Virtex-4 FPGAs. The results showed that the Grigoryan FFT operated at higher sampling rates and was faster than the Cooley-Tukey FFT. Additionally, the Virtex-5 FPGA provided the highest speed for implementing the Grigoryan FFT compared
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
1. SPIRAL: Current Status
Markus Püschel
Students
Faculty
• José Moura (CMU)
• Jeremy Johnson (Drexel)
• Robert Johnson (MathStar)
• David Padua (UIUC)
• Viktor Prasanna (USC)
• Markus Püschel (CMU)
• Manuela Veloso (CMU)
• Gavin Haentjens (CMU)
• Pinit Kumhom (Drexel)
• Neungsoo Park (USC)
• David Sepiashvili (CMU)
• Bryan Singer (CMU)
• Yevgen Voronenko (Drexel)
• Edward Wertz (CMU)
• Jianxin Xiong (UIUC)
Collaborators
• Christoph Überhuber (TU Vienna)
• Franz Franchetti (TU Vienna)
http://www.ece.cmu.edu/~spiral
2. Sponsor
Work supported by DARPA (DSO), Applied & Computational
Mathematics Program, OPAL, through grant managed by
research grant DABT63-98-1-0004 administered by the Army
Directorate of Contracting.
3. Moore’s Law
and High(est) Performance
Scientific Computing
(single processor, off-the-shelf)
Moore’s Law:
processor-memory bottleneck
short life cycles of computers
very complex architectures
• vendor specific
• special instructions (MMX, SSE, FMA, …)
• undocumented features
Consequences for software/algorithms:
arithmetic cost model not accurate for predicting runtime
better performance models hard to get
best code is machine dependent (registers/caches size, structure)
hand-tuned code becomes obsolete as fast as it is written
compiler limitations
full performance requires (in part) assembly coding
Portable performance requires automation
4. SPIRAL
Automates
Implementation
cuts development costs
code less error-prone
Optimization
systematic exploration of alternatives both at
algorithmic and code level
Platform-Adaptation
takes advantage of architecture specific features
porting without loss of performance
of DSP algorithms
are performance critical
A library generator for highly optimized
signal processing algorithms
5. SPIRAL system
goes for a coffee
ian
tic
a
controls
algorithm generation
Formula Generator
fast algorithm
as SPL formula
ert
Exp m
SPL Compiler er
ram
rog
P
controls
implementation options
C/Fortran/SIMD
code
platform-adapted
implementation
Search Engine
hem
at
M
specifies
runtime on given platform
comes back
(or an espresso for small transforms)
SPIRAL
DSP transform
user
8. DSP Algorithms: Terminology
Transform
DFTn
Rule
DFTnm → ( DFTn ⊗ I m ) ⋅ D ⋅ ( I n ⊗ DFTm ) ⋅ P
parameterized matrix
• a breakdown strategy
• product of sparse matrices
DFT 8
Ruletree
DFT 2
DFT 4
DFT 2
Formula
DFT 2
• recursive application of rules
• uniquely defines an algorithm
• efficient representation
• easy manipulation
DFT8 = ( F2 ⊗ I 4 ) ⋅ D ⋅ ( I 2 ⊗ ( I 2 ⊗ F2 ) ) ⋅ P
• few constructs and primitives
• uniquely defines an algorithm
• can be translated into code
9. DSP Transforms
discrete Fourier transform
Walsh-Hadamard transform
DFTn = [ exp( 2kliπ / n ) ]
WHT2k = DFT2 ⊗ ⊗ DFT2
DCT ( II ) n = [ cos( k ( l + 1 / 2 )π / n ) ]
DCT ( IV ) n = [ cos( ( k + 1 / 2) (l + 1 / 2)π / n ) ]
discrete cosine and sine
Transforms (16 types)
modified discrete
cosine transform
DST ( I ) n = [ sin ( klπ / n ) ]
MDCTn×2 n = [ cos( ( k + ( n + 1) / 2)(l +1 / 2)π / n ) ]
two-dimensional transform
T ⊗T
Others: filters, discrete wavelet transforms, Haar, Hartley, …
10. Rules = Breakdown Strategies
(
)
DCT2( II ) → diag 1,1 / 2 ⋅ F2
(
)
DCTn( II ) → P ⋅ DCTn(/II ) ⊕ DCTn(/IV ) ⋅ ( I n / 2 ⊗ F2 )
2
2
base case
Q
recursive
DCTn( IV ) → S ⋅ DCTn( II ) ⋅ D
translation
DCTn( IV ) → M 1 M r
DFTn → B ⋅ ( DCTn(/I2) ⊕ DSTn(/I2) ) ⋅ C
iterative
recursive
DFTnm → ( DFTn ⊗ I m ) ⋅ D ⋅ ( I n ⊗ DFTm ) ⋅ P
recursive
Fn (h) → ( I n / d ⊗ k I d + k ) ⋅ ( I n / d ⊗ Fd (h))
recursive
Fn (h) → Circ (h ) ⋅ E
recursive
DWTn (W ) → ( DWTn / 2 (W ) ⊕ I n / 2 ) ⋅ P ⋅ ( I n / 2 ⊗ k W ) ⋅ E
recursive
WHT2n → ∏ ( I 2n1+K+ni −1 ⊗ WHT2ni ⊗ I 2ni +1+K+nt )
iterative/
recursive
MDCTn×2 n → S ⋅ DCTn( IV ) ⋅ P
translation
n
i =1
16. SPL Compiler: Summary
SPL Program
SPL Formula
Symbol Definition Template Definition
Parsing
Abstract Syntax Tree
Symbol Table
Template Table
Intermediate Code Generation
I-Code
Intermediate Code Restructuring
I-Code
Optimization
I-Code
Target Code Generation
C, FORTRAN function
Built-in optimizations:
single static assignment code
no reuse of temporary vars
only scalar temporary vars
constants precomputed
limited CSE
Extensible through templates
18. Why Search?
DCT, type IV, size 16
~31000 formulas
• maaaany different formulas
• large spread in runtimes, even for modest size
• not due to arithmetic cost
• best formula is platform-dependent
19. Search Methods available in SPIRAL
Exhaustive Search
Dynamic Programming (DP)
Random Search
Hill Climbing
STEER (similar to a genetic algorithm)
Possible
Sizes
Exhaust Very small
Formulas
Timed
Results
All
Best
DP
All
10s-100s
(very) good
Random
All
User decided
fair/good
Hill Climbing
All
100s-1000s
Good
STEER
All
100s-1000s
(very) good
Search over
• algorithm space and
• implementation options (degree of unrolling)
21. Experimental Results (C code)
search methods
(applicable to all transforms)
high performance code
(compared with FFTW)
different transforms
generated high quality code
22. SPIRAL System
Available for download (v3.1): www.ece.cmu.edu/~spiral
Easy installation (Unix: configure/make; Windows: install shield)
Unix/Linux and Windows 98/ME/NT/2000/XP
Current transforms: DFT, DHT, WHT, RHT, DCT/DST type I – IV,
MDCT, Filters, Wavelets, Toeplitz, Circulants
Extensible
New version (4.0) in preparation
24. Learning to Generate Fast Algorithms
• Learns from given dataset (formulas+runtimes) how to design a
fast algorithm (breakdown strategy)
• Learns from a transform of one size, generates the best algorithm
for many sizes
• Tested for DFT and WHT
25. SIMD Short Vector Extensions
vector length = 4
(4-way)
x
+
Extension to instruction set architecture
Available on most current architectures
(SSE on Pentium, AltiVec on Motorola G4)
Originally for multimedia (like MMX for integers)
Requires fine grain parallelism
Large potential speed-up
Problems:
SIMD instructions are architecture specific
No common API (usually assembly hand coding)
Performance very sensitive to memory access
Automatic vectorization very limited
very difficult to use
26. Vector code generation from SPL formulas
Naturally vectorizable construct
A ⊗ I4
A
vector length
x
y
(Current) generic construct completely vectorizable:
k
∏ P D ( A ⊗ Iυ ) E Q
i
i
i
i =1
i
i
Pi, Qi
Di, Ei
Ai
ν
permutations
diagonals
arbitrary formulas
SIMD vector length
Vectorization in two steps:
1. Formula manipulation using manipulation rules
2. Code generation (vector code + C code)
27. Generated Vector Code DFTs: Pentium 4
7
6
Spiral SSE
gflops
5
Intel MKL interl.
4
FFTW 2.1.3
Spiral C and F95
3
Spiral F95 vect
SIMD-FFT
2
1
0
4
5
6
7
8
9
10
11
12
13
14
n
DFT 2^n, Pentium 4, 2.53 GHz, using Intel C compiler 6.0
speedups (to C code) up to factor of 3.3
beats hand-tuned vendor library
28. Generated Vector Code, Other Transforms
WHT
normalized runtime
normalized runtime
2-dim DCT
transform size
transform size
speedups up to factor of 2.5
29. Flexible Loop Interleaving (Runtime Gain WHT)
Athlon XP: up to 55%
Alpha 21264: up to 70%
Pentium 4: up to 45%
UltraSparc III: up to 60%
30. Parallel Code Generation: Example WHT
10
PowerPC RS64 III
Speedup
8
6
4
1 thread
8 threads
10 threads
2
0
1
6
11
16
WHT size log(N)
21
26
Parallelized constructs:
In ⊗ A, A ⊗ In
32. Filters and Wavelets
New constructs: row/column overlapped tensor product
A
A
A
I n ⊗k A =
A
I n ⊗k A =
A
A
A
Examples for rules:
Fn (h) → ( I n / d ⊗ k I d + k ) ⋅ ( I n / d ⊗ Fd (h))
DWTn (W ) → ( DWTn / 2 (W ) ⊕ I n / 2 ) ⋅ P ⋅ ( I n / 2 ⊗ k W ) ⋅ E
A
33. Conclusions
Automatic code generation for the entire domain of
(linear) DSP algorithms
Portable high performance across platforms and
across time
Integration of math (high) level and implementation
(low) level
Intelligence through search and learning in the
space of alternatives
37. Generating Parallel Programs
Interpret constructs such as In ⊗ A as parallel operations and
transform formulas to obtain maximal parallelism.
Explore alternative data access patterns mathematically (e.g.
different permutations in matrix factorizations)
Prototype implementation using WHT
Build on existing sequential package
SMP implementation using OpenMP (IPDPS’02)
90% efficiency obtained on 12 processor PowerPC RS64 III
Distributed memory implementation using MPI (POHLL’02)
38. Comparison of Parallel DDL Schemes
10
Speedup
PowerPC RS64 III
10 threads
8
6
4
2
0
1
6
11
16
21
WHT size log(N)
26
Best sequential with
DDL
Parallel without DDL
Coarse-grained DDL
Fine-grained DDL
with ID shift
40. Performance of Digit Permutations on CRAY T3E
Distribution of Global Tensor Permutations on 128 Processors (shmem put)
1200
Number of tensor permutations
1000
800
600
400
200
0
0
50
100
150
200
250
Bandwidth in MB/sec/node
300
350
41. Architecture Framework
P4 ( I 8 ⊗ F2 )T4 P4− 1 P3 ( I 8 ⊗ F2 )T3 P3− 1 P2 ( I 8 ⊗ F2 )T2 P2− 1 P1 ( I 8 ⊗ F2 )T1P1− 1 Pd
Host
I/O Interface
Parameters: Dimension,
Pd
I/O Interface
M = Memory
AG = Address Generator
CU = Computation Unit
Interconnection Network
Parameters: Pi, 1 ≤ i ≤ n,
no. of processor
AG
CU AG
CU AG
M
M
CU AG
... M
CU AG
M
Parameters: Dimension,
and Ti, 1 ≤ i ≤ n, no. of
processor 2m
CU
47. Classical Code Generation System
given
DSP Transform
(DFT, DCT, Wavelets etc.)
Math/Algorithm
Expert
Expert
Programmer
Performance
Evaluation
given
Computing Platform
(Pentium III, Pentium 4, Athlon,
SUN, PowerPC, Alpha, … )
adapted
implementation
48. Algorithms = Ruletrees = Formulas
DCT8( II )
DCTn( II ) → P ⋅ ( DCTn(/II ) ⊕ DCTn(/IV ) ) ⋅ ( F2 ⊗ I n / 2 )
2
2
R1
DCT4( IV )
DCT4( II )
R1
DCT2( II )
DCT4( II )
DCT2( IV )
R3
F2
DCTn( IV ) → P ⋅ DCTn( II ) ⋅ S
R6
R6
( II )
2
R1
DST
R4
DCT2( II )
F2
R3
DCT2( II ) →
1
⋅ F2
2
DCT2( IV )
F2
R6
DST2( II )
R4
F2
49. Mathematical Framework: Summary
fast algorithms represented as ruletrees (easy generation/manipulation)
and as formulas (can be translated into code)
formulas built from few constructs and primitives
many different algorithms/formulas generated from few rules
(combinatorial explosion)
these algorithms are (essentially) equal in arithmetic cost,
but differ in data flow
50. Formula Generation
data base (extensible!)
data type
Formula Generator
rules
recursive
application
transforms
control search engine
ruletrees
formulas
runtime
export
formula
translation
(spl compiler)
translation
written in GAP/AREP (computer algebra system)
all computation/manipulation is symbolic
exact arithmetic
easy extensible rule and transform data base
verification of rules and formulas
cut here for other
optimization problems
51. Number of Formulas/Algorithms
k
# DFT, size 2^k
# DCT-IV, size 2^k
1
2
3
4
5
6
7
8
9
1
6
40
296
27744
162570361280
~1.01 • 10^27
~2.31 • 10^61
~2.86 • 10^133
1
10
126
31242
1924443362
7343815121631354242
~1.07 • 10^38
~2.30 • 10^76
~1.06 • 10^153
differ in data flow not in arithmetic cost
exponential search space
52. Extensibility of SPIRAL
New transforms are readily included on the high level
(easy, due to SPIRAL’s framework)
New constructs and primitives (potentially required by radically
different transforms) are readily included in SPL
(moderate effort, due to template mechanism)
New instructions sets available (e.g., SSE) are included by
extending the SPL compiler
(doable one time effort)
53. Generated Vector Code DFTs: Pentium III
1.8
1.6
1.4
gflops
1.2
Intel MKL
Spiral SSE
Spiral C/F95 vect
Spiral C/F95
FFTW 2.1.3
1
0.8
0.6
0.4
0.2
0
4
5
6
7
8
9
10
11
12
13
n
DFT 2^n, Pentium III, 1 GHz, using Intel C compiler 6.0
speedups (to C code) up to factor of 2.5
Editor's Notes
This graph shows the parallel speedup of the WHT transform with 10 threads at different data sizes. The effect of granularity on parallel pseudo transpose is obvious. The fine-grained is better than the coarse-grained. It is different from the transform part.
The yellow line shows the speedup of work-sharing OpenMP. There is only 3 times speedup even though 10 threads are used. This indicates, although OpenMP provides the convenience to parallelize iteration automatically, the efficiency could be very low depending on the nature of the problem.
The dataflow of the conjugating form of the Pease Algorithm.