vector QUANTIZATION
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Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword is the center of a Voronoi region that contains all vectors closest to that codeword. The LBG algorithm trains a vector quantizer by iteratively adjusting codewords to minimize distortion based on a training set. Tree-structured vector quantization further improves efficiency by recursively partitioning the codebook into binary tree structure to reduce distance comparisons at the cost of potential increases in distortion and storage requirements for additional test vectors.
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vector QUANTIZATION
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization improves efficiency by recursively partitioning the codebook into binary groups defined by test vectors. This reduces the number of distance comparisons needed at the cost of potential increases in distortion and storage requirements.
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This document provides an overview of satellite communications fundamentals. It discusses how satellites provide capabilities not available through landlines, such as mobility and quick implementation. However, satellites are not always the most cost effective solution due to limited frequency spectrum and spatial capacity. The document describes different types of satellite services and configurations, including geostationary and non-geostationary satellites. It also covers topics like frequency reuse, earth station antennas, and satellite link delays.
euler theorm
Euler's theorem states that for any plane graph, the number of vertices (v) minus the number of edges (e) plus the number of faces (f) equals 2. The document proves this theorem by considering a minimal tree (T) within the graph and its dual tree (D), showing that the number of edges of T and D sum to the total edges (e) of the original graph. Some applications of the theorem are that any plane graph contains an edge of degree 5 or higher and any finite set of points not all on a line contains a line with exactly two points.
DIC_video_coding_standards_07
H.120 was the first digital video coding standard developed in 1984. H.261 in the late 1980s was the first widespread success and established the modern structure for video compression that is still used today. MPEG-1 and MPEG-2/H.262 built upon H.261 with improvements like bidirectional prediction and half-pixel motion compensation. H.263 further enhanced compression performance and is now dominant for videoconferencing, adding features such as overlapped block motion compensation.
video_compression_2004
The document provides an overview of video coding techniques used in video compression standards. It discusses how video compression exploits both the spatial and temporal redundancy in video signals. Key techniques covered include motion-compensated prediction, where a current frame is predicted from previously coded reference frames using motion vectors, and block-based motion estimation to determine the motion vectors. The document also outlines the generic architecture of video compression systems, which apply representation, quantization, and binary encoding steps to remove redundancy from video signals.
quantization
The document discusses quantization in analog-to-digital conversion. It describes the three processes of A/D conversion as sampling, quantization, and binary encoding. Quantization involves mapping amplitude values into a set of discrete values using a quantization interval or step size. The document discusses uniform quantization and how the quantization levels are determined. It also covers non-uniform quantization and provides examples and MATLAB code demonstrations of audio signal quantization.
Advformat_0609
This document contains information and forms related to the UGC National Eligibility Test for Junior Research Fellowship and Eligibility for Lectureship that will take place on June 28, 2009. It includes an application form to apply for the exam, instructions on filling out the form, an attendance slip for the exam day, and an admission card with exam details. The forms request information such as educational qualifications, exam subject and center, and contact details.
Discrete cosine transform
The document discusses DCT/IDCT concepts and applications. It provides an introduction to DCT and IDCT, explaining that they are used widely in video and audio compression. It describes the DCT and IDCT functions and how they work to transform signals between spatial and frequency domains. Examples of one-dimensional and two-dimensional DCT/IDCT equations are also given. Finally, common applications of DCT/IDCT compression techniques are listed, such as in DVD players, cable TV, graphics cards, and medical imaging systems.
DVB_Arch
This document provides an overview of service information (SI) in digital video broadcasting (DVB) systems, including sections like the network information section (NIT), service description section (SDT), bouquet association section (BAT), program association section (PAT), conditional access section (CAT), transport stream description section (TSDT), event information section (EIT), and running status section (RST). It includes syntax diagrams and details for each section, such as table IDs, section lengths, descriptors, and other fields. It also provides the PID and refresh interval requirements for each table type.
intro_dgital_TV
This document provides an introduction to digital television. It discusses analog TV standards and the conversion to digital with ITU-R BT.601 and BT.709 standards defining digital video formats. It also describes MPEG transport streams, the DVB system for content delivery over satellite, cable and terrestrial networks, and conditional access systems. Packetized elementary streams (PES) and program specific information (PSI) tables are also introduced.
IntrRSCode
This document provides an introduction to Reed-Solomon codes, which are word-oriented, non-binary BCH codes that are simple, robust, and perform well for burst errors. Reed-Solomon codes use Galois field techniques to encode data into blocks of length 2^m - 1 by adding 2t parity check words, allowing the correction of t errors. The encoding and decoding procedures make use of a generator polynomial, Berlekamp-Massey algorithm, Chien search, and Forney algorithm. Future work may include more flexible generator polynomials or converting C54x codes to C55x codes.
rsa-1
RSA is a widely used public-key cryptosystem. It works by generating a public and private key pair. The public key is used for encryption and digital signatures while the private key is used for decryption and signature verification. Key generation involves finding two prime numbers p and q, computing the modulus n as their product, and using these values to calculate the public and private exponents e and d respectively.
Basic of BISS
This document discusses the basics of BISS scrambling. It describes BISS mode 1, which uses a session word, and BISS mode E, which encrypts the session word using an identifier and encryption algorithm. BISS mode E provides an additional layer of protection for transmitting the session word. The document also covers calculating the encrypted session word, using buried and injected identifiers, and how to operate scramblers in the different BISS modes.
whitepaper_mpeg-if_understanding_mpeg4
The document provides an overview of MPEG-4, a standard that offers both advanced audio and video codecs as well as tools for combining multimedia such as audio, video, graphics and interactivity. MPEG-4's codecs provide high compression efficiency, with its AVC video codec offering half the bitrate of MPEG-2 for similar quality. Its tools allow for rich interactive media experiences by combining different media types. Manufacturers and operators have adopted MPEG-4 due to its excellent performance, open development process, compatibility between implementations, and ability to encode once and play anywhere.
Discrete cosine transform
The document discusses DCT/IDCT concepts and applications. It provides an introduction to DCT and IDCT, explaining that they are used widely in video and audio compression. It describes the DCT and IDCT functions and how they work to transform signals between spatial and frequency domains. Examples of one-dimensional and two-dimensional DCT/IDCT equations are also given. Finally, common applications of DCT/IDCT compression techniques are listed, such as in DVD players, cable TV, graphics cards, and medical imaging systems.
fundamentals_satellite_communication_part_1
This document provides an overview of satellite communications fundamentals. It discusses how satellites provide capabilities not available through landlines, such as mobility and quick implementation. However, satellites are not always the most cost effective solution due to limited frequency spectrum and spatial capacity. The document describes different types of satellite services and configurations, including geostationary and non-geostationary satellites. It also covers topics like frequency reuse, earth station antennas, and satellite link delays.
euler theorm
Euler's theorem states that for any plane graph, the number of vertices (v) minus the number of edges (e) plus the number of faces (f) equals 2. The document proves this theorem by considering a minimal tree (T) within the graph and its dual tree (D), showing that the number of edges of T and D sum to the total edges (e) of the original graph. Some applications of the theorem are that any plane graph contains an edge of degree 5 or higher and any finite set of points not all on a line contains a line with exactly two points.
DVB_Arch
This document discusses service information (SI) in DVB systems, specifically the discontinuity information section. It provides packet syntax diagrams for an MPEG-2 transport stream, the network information section, service description section, and bouquet association section, which are used to deliver metadata about available TV and radio channels and services. It also includes the syntax for a discontinuity information section, which is used to signal discontinuities in MPEG program streams that are caused by operations like splicing.
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This summary provides the key points about the document in 3 sentences:
The document presents a method for obtaining the exact probability of error for block codes using soft-decision decoding and the eigenstructure of the code correlation matrix. It shows that under a unitary transformation, the performance evaluation of a block code becomes a one-dimensional problem involving only the dominant eigenvalue and its corresponding eigenvector. Simulation results demonstrate good agreement with the analysis, validating the method for computing the bit error rate of block codes based on the properties of the code correlation matrix.
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- Dimensionality reduction techniques assign instances to vectors in a lower-dimensional space while approximately preserving similarity relationships. Principal component analysis (PCA) is a common linear dimensionality reduction technique.
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Fundamentals of Machine Learning.pptx
This document provides an overview of fundamentals of linear algebra and vector calculus concepts that are important for machine learning. It begins with an introduction to Euclidean spaces and vectors, then covers key linear algebra topics like matrices, matrix operations, and solving systems of linear equations. It also discusses vectors norms, eigenvalues/eigenvectors, and the Cholesky decomposition. Finally, it introduces concepts in vector calculus like derivatives, gradients, Hessians, and their application to backpropagation in neural networks. The document is intended to provide machine learning practitioners and students with the essential mathematical foundations.
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Kohonen networks are a type of self-organizing map (SOM) neural network that is effective for clustering analysis. SOMs reduce the dimensionality of input data while preserving the topological properties of the input. Kohonen networks learn through competitive learning where output nodes compete to be activated by an input observation. The weights of the winning node and its neighbors are adjusted to be more similar to the input values. This allows the network to cluster similar input patterns together on the output map. The document provides a detailed example of how Kohonen networks work through competition, cooperation, and adaptation steps to cluster a sample dataset.
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This document provides an overview and introduction to representation learning of text, specifically word vectors. It discusses older techniques like bag-of-words and n-grams, and then introduces modern distributed representations like word2vec's CBOW and Skip-Gram models as well as the GloVe model. The document covers how these models work, are evaluated, and techniques to speed them up like hierarchical softmax and negative sampling.
Deep Learning Bangalore meet up
Deep dive into the world of word vectors. We will cover - Bigram model, Skip-gram, CBOW, GLO. Starting from simplest models, we will journey through key results and ideas in this area.
https://www.meetup.com/Deep-Learning-Bangalore/events/239996690/
Convolutional Error Control Coding
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IEEE DSP Workshop 2011
This document proposes a new approach to quantization that optimizes for classification accuracy rather than signal fidelity alone. It introduces a distortion measure that trades off mean squared error and the Kullback-Leibler divergence between the classifier's probability of a signal's class before and after quantization. Simulations show this approach dedicates codebook points near class boundaries, improving classification accuracy over a standard mean squared error quantizer, especially at high bit rates, while maintaining good signal distortion. The method provides an effective way to quantize signals for classification tasks.
IEEE DSP Workshop 2011
This document proposes a new approach to quantization that optimizes for classification accuracy rather than signal fidelity alone. It introduces a distortion measure that trades off mean squared error and the Kullback-Leibler divergence between the classifier's probability of a signal's class before and after quantization. Simulations show this approach dedicates codebook points near class boundaries, improving classification accuracy over a standard mean squared error quantizer, especially at high bit rates, while maintaining good signal distortion. The method provides an effective way to quantize signals for classification tasks.
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This document discusses convolutional codes. It defines basic concepts like constraint length and generator polynomials that define convolutional codes. It describes representations of convolutional codes using state diagrams and trellis diagrams. It also discusses decoding convolutional codes using the Viterbi algorithm, which finds the most likely path through the trellis. The document concludes by discussing properties of convolutional codes like free distance, which is the minimum Hamming distance between codewords.
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This document discusses digital set-top boxes (STBs) and related standards. It covers:
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This document discusses image compression using the discrete cosine transform (DCT). It develops simple Mathematica functions to compute the 1D and 2D DCT. The 1D DCT transforms a list of real numbers into elementary frequency components. It is computed via matrix multiplication or using the discrete Fourier transform with twiddle factors. The 2D DCT applies the 1D DCT to rows and then columns of an image, making it separable. These functions illustrate how Mathematica can be used to prototype image processing algorithms.
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Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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vector QUANTIZATION
- 1. Vector Quantization Aniruddh Tyagi 02-06-12
- 2. Voronoi Region • Blocks: o A sequence of audio. o A block of image pixels. Formally: vector example: (0.2, 0.3, 0.5, 0.1) • A vector quantizer maps k-dimensional vectors in the vector space Rk into a finite set of vectors Y = {yi: i = 1, 2, ..., N}. Each vector yi is called a code vector or a codeword. and the set of all the codewords is called a codebook. Associated with each codeword, yi, is a nearest neighbor region called Voronoi region, and it is defined by: • The set of Voronoi regions partition the entire space Rk .
- 3. Two Dimensional Voronoi Diagram Codewords in 2-dimensional space. Input vectors are marked with an x, codewords are marked with red circles, and the Voronoi regions are separated with boundary lines.
- 4. The Schematic of a Vector Quantizer
- 5. Compression Formula • Amount of compression: o Codebook size is K, input vector of dimension L o In order to inform the decoder of which code vector is selected, we need to use bits. E.g. need 8 bits to represent 256 code vectors. o Rate: each code vector contains the reconstruction value of L source output samples, the number of bits per sample would be: . o Sample: a scalar value in vector. o K: level of vector quantizer.
- 6. VQ vs SQ Advantage of VQ over SQ: • For a given rate, VQ results in a lower distortion than SQ. • If the source output is correlate, vectors of source output values will tend to fall in clusters. o E.g. Sayood’s book Exp 9.3.1 • Even if no dependency: greater flexibility. o E.g. Sayood’s book Exp 9.3.2
- 7. Algorithms • Lloyd algorithm: pdf-optimized quantizer, assume that distribution is known • LBG: (VQ) o Continuous (require integral ooperation) o Modified: with training set.
- 8. LBG Algorithm – Determine the number of codewords, N, or the size of the codebook. – Select N codewords at random, and let that be the initial codebook. The initial codewords can be randomly chosen from the set of input vectors. – Using the Euclidean distance measure clusterize the vectors around each codeword. This is done by taking each input vector and finding the Euclidean distance between it and each codeword. The input vector belongs to the cluster of the codeword that yields the minimum distance.
- 9. LBG Algorithm (contd.) 4.Compute the new set of codewords. This is done by obtaining the average of each cluster. Add the component of each vector and divide by the number of vectors in the cluster. where i is the component of each vector (x, y, z, ... directions), m is the number of vectors in the cluster. 5.Repeat steps 2 and 3 until the either the codewords don't change or the change in the codewords is small.
- 10. Other Algorithms • Problems: LBG is a greedy algorithm, may fall into Local minimum. • Four methods selecting initial vectors: o Random o Splitting ( with perturbation vector) Animation o Train with different subset o PNN (pairwise nearest neighbor) • Empty cell problem: o No input correspond to a output vector o Solution: give to other clusters, e.g. most populate cluster.
- 11. LBG for image compression • Taking blocks of images as vector L=NM. • If K vectors in code book: o need to use bits. o Rate: • The higher the value K, the better quality, but lower compression ratio. • Overhead to transmit code book: • Train with a set of images.
- 12. Rate_Dimension Product • Rate-dimension product o The size of the codebook increase exponentially with the rate. o Suppose we want to encode a source using R bits/sample. If we use an L-d quantizer, we would group L samples together into vectors. This means that we would have RL bits available to represent wach vector. o With RL bits, we can represent 2^(RL) output vectors.
- 13. Tree structured VQ • Set vectors in different quadrant. Only signs of vectors need to be compared. Thus reduce the number of comparisons by 2^L for L-d vector problem. • It works well for symmetric distribution. But not when we lose more and more symmetry.
- 14. Tree Structured Vector Quantizer • Extend to non-symmetric case: o Divide the set of output points into two groups, g0 and g1, and assign to each group a test vector s.t. output points in each group are closer to test vector assigned to that group than to the test vector assigned to the other group. o Label the two test vectors 0 and 1. o When we got an input vector, compare it against the test vectors. Depending on the outcome, the input is compared to the output points associated with the test vector closest to the input. o After these two comparisons, we can discard half of the output points. o Comparison with the test vectors takes the place of looking at the signs of the components to decide which set of output points to discard from contention. o If the total number of output points is K, we make( K/2)+2 comparisons instead of K comparisons. o Can continue to expand the number of groups. Finally: 2logK comparisons instead of K.( 2 comparisons with the test vectors and a total of logK stages
- 15. Tree Structured VQ (continued) • Since the test vectors are assigned to groups: 0, 1, 00,01,10,11,000,001,010,011,100,101,110,111 etc. which are the nodes of a binary tree, the VQ has the name “Tree Structured VQ”. • Penalty: o Possible increase in distortion: it is possible that at some stage the input is closer to one test vector while at the same time being closest to an output belonging to the rejected group. o Increase storage: output points from VQ codebook plus the test vectors.
- 16. Additional Links • Slides are adapted from: http://www.data-compression.com and http://www.geocities.com/mohamedqase m/vectorquantization/vq.html