Hi.., I had work out on this research paper in duration my master degree(MCA) and Successfully present at 6th International Conference under "Science Engineering Technology (SET) " in may-2013 in VIT University.
A Survey on Block Matching Algorithms for Video Coding Yayah Zakaria
Block matching algorithm (BMA) for motion estimation (ME) is the heart to many motion-compensated video-coding techniques/standards, such as ISO MPEG-1/2/4 and ITU-T H.261/262/263/264/265, to reduce the temporal redundancy between different frames. During the last three decades,
hundreds of fast block matching algorithms have been proposed. The shape and size of search patterns in motion estimation will influence more on the searching speed and quality of performance. This article provides an overview of the famous block matching algorithms and compares their computational complexity and motion prediction quality.
This document describes an implementation of fast image convolution using Winograd's minimal filtering algorithm for 3x3 filters. The implementation combines C code with BLAS calls for GEMM. It is optimized for Intel Xeon Phi processors and uses Intel MKL for BLAS calls. Benchmarking shows the implementation achieves 10% greater overall performance than MKL convolution and can be up to 1.5x faster for some layers and up to 4x slower for others, indicating potential for a hybrid approach. High-bandwidth memory on Intel Xeon Phi significantly improves efficiency of fast convolution.
The document describes the Pochoir stencil compiler, which allows programmers to write specifications for stencil computations in a domain-specific language embedded in C++. The Pochoir compiler then translates these specifications into high-performance parallel Cilk code using an efficient cache-oblivious algorithm called TRAP. Benchmark results show that the Pochoir-generated code runs 2-10 times faster than standard parallel loop implementations for a variety of stencil computations.
This document summarizes an approach to perform online Gaussian process regression using random feature selection in order to address the computational challenges of traditional GPR. It proposes combining random feature mapping with online Bayesian linear regression to develop a fast approximate GPR model that can perform online learning from streaming data. The goal is to apply this method to motion planning for a 7-DOF robotic arm. The algorithm will be implemented in MATLAB/Octave and tested on inverse dynamics problems using a Barrett Technology robot arm.
This document analyzes Bernstein's proposed circuit-based approach for the matrix step of the number field sieve integer factorization method. It finds that Bernstein overestimated the improvement in factoring larger integers, which would be a factor of 1.17 larger rather than 3.01 as claimed. The document also proposes an improved circuit design based on a new mesh routing algorithm. It estimates that for 1024-bit RSA, the matrix step could be completed in a day using a few thousand dollars of custom hardware, but that the relation collection step still determines the practical security of RSA.
(Paper Review)3D shape reconstruction from sketches via multi view convolutio...MYEONGGYU LEE
review date: 2019/03/20 (by Meyong-Gyu.LEE @Soongsil Univ.)
Korean review of '3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks'(CVPR 2017)
Full search motion estimation calculates matching errors for all candidate motion vectors within the search range to find the best motion vector, resulting in huge computational load. For a search range of 1 and block size of 16, it requires over 18,000 operations just for calculating sum of absolute differences. Fractional pixel motion estimation further increases the computational load by searching around the integer motion vector. Due to the heavy computations, full search is not practical for implementation.
Distributed approximate spectral clustering for large scale datasetsBita Kazemi
The document proposes a distributed approximate spectral clustering (DASC) algorithm to process large datasets in a scalable way. DASC uses locality sensitive hashing to group similar data points and then approximates the kernel matrix on each group to reduce computation. It implements DASC using MapReduce and evaluates it on real and synthetic datasets, showing it can achieve similar clustering accuracy to standard spectral clustering but with an order of magnitude better runtime by distributing the computation across clusters.
A Survey on Block Matching Algorithms for Video Coding Yayah Zakaria
Block matching algorithm (BMA) for motion estimation (ME) is the heart to many motion-compensated video-coding techniques/standards, such as ISO MPEG-1/2/4 and ITU-T H.261/262/263/264/265, to reduce the temporal redundancy between different frames. During the last three decades,
hundreds of fast block matching algorithms have been proposed. The shape and size of search patterns in motion estimation will influence more on the searching speed and quality of performance. This article provides an overview of the famous block matching algorithms and compares their computational complexity and motion prediction quality.
This document describes an implementation of fast image convolution using Winograd's minimal filtering algorithm for 3x3 filters. The implementation combines C code with BLAS calls for GEMM. It is optimized for Intel Xeon Phi processors and uses Intel MKL for BLAS calls. Benchmarking shows the implementation achieves 10% greater overall performance than MKL convolution and can be up to 1.5x faster for some layers and up to 4x slower for others, indicating potential for a hybrid approach. High-bandwidth memory on Intel Xeon Phi significantly improves efficiency of fast convolution.
The document describes the Pochoir stencil compiler, which allows programmers to write specifications for stencil computations in a domain-specific language embedded in C++. The Pochoir compiler then translates these specifications into high-performance parallel Cilk code using an efficient cache-oblivious algorithm called TRAP. Benchmark results show that the Pochoir-generated code runs 2-10 times faster than standard parallel loop implementations for a variety of stencil computations.
This document summarizes an approach to perform online Gaussian process regression using random feature selection in order to address the computational challenges of traditional GPR. It proposes combining random feature mapping with online Bayesian linear regression to develop a fast approximate GPR model that can perform online learning from streaming data. The goal is to apply this method to motion planning for a 7-DOF robotic arm. The algorithm will be implemented in MATLAB/Octave and tested on inverse dynamics problems using a Barrett Technology robot arm.
This document analyzes Bernstein's proposed circuit-based approach for the matrix step of the number field sieve integer factorization method. It finds that Bernstein overestimated the improvement in factoring larger integers, which would be a factor of 1.17 larger rather than 3.01 as claimed. The document also proposes an improved circuit design based on a new mesh routing algorithm. It estimates that for 1024-bit RSA, the matrix step could be completed in a day using a few thousand dollars of custom hardware, but that the relation collection step still determines the practical security of RSA.
(Paper Review)3D shape reconstruction from sketches via multi view convolutio...MYEONGGYU LEE
review date: 2019/03/20 (by Meyong-Gyu.LEE @Soongsil Univ.)
Korean review of '3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks'(CVPR 2017)
Full search motion estimation calculates matching errors for all candidate motion vectors within the search range to find the best motion vector, resulting in huge computational load. For a search range of 1 and block size of 16, it requires over 18,000 operations just for calculating sum of absolute differences. Fractional pixel motion estimation further increases the computational load by searching around the integer motion vector. Due to the heavy computations, full search is not practical for implementation.
Distributed approximate spectral clustering for large scale datasetsBita Kazemi
The document proposes a distributed approximate spectral clustering (DASC) algorithm to process large datasets in a scalable way. DASC uses locality sensitive hashing to group similar data points and then approximates the kernel matrix on each group to reduce computation. It implements DASC using MapReduce and evaluates it on real and synthetic datasets, showing it can achieve similar clustering accuracy to standard spectral clustering but with an order of magnitude better runtime by distributing the computation across clusters.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using clustering algorithms to construct ontologies from text documents. It begins with an introduction to semantic search, ontologies in the semantic web, and clustering. It then describes the ROCK clustering algorithm in detail. The main tasks to perform are preprocessing text documents, normalizing term weights, applying latent semantic indexing via singular value decomposition, and using the ROCK clustering algorithm. The goal is to group similar documents into clusters to help construct an ontology from the unstructured text data.
This document presents and compares three approximation methods for thin plate spline mappings that reduce the computational complexity from O(p3) to O(m3), where m is a small subset of points p. Method 1 uses only the subset of points to estimate the mapping. Method 2 uses the subset of basis functions with all target values. Method 3 approximates the full matrix using the Nyström method. Experiments on synthetic grids show Method 3 has the lowest error, followed by Method 2, with Method 1 having the highest error. The three methods trade off accuracy, computation time, and the ability to do principal warp analysis.
New Chaotic Substation and Permutation Method for Image Encryptiontayseer Karam alshekly
New Chaotic Substation and Permutation Method for Image Encryption is introduced based on combination between Block Cipher and chaotic map. The new algorithm encrypts and decrypts a block of 500 byte. Each block is firstly permuted by using the hyper-chaotic map and then the result is substituted using 1D Bernoulli map. Finally the resulted block is XORed with the key block. The proposed cipher image subjected to number of tests which are the security analysis (key space analysis and key sensitivity analysis) and statistical attack analysis (histogram, correlation, and differential attack and information entropy) and all results show that the proposed encryption scheme is secure because of its large key space; it’s highly sensitivity to the cipher keys and plain-images.
Training and Inference for Deep Gaussian ProcessesKeyon Vafa
The document discusses training and inference for deep Gaussian processes (DGPs). It introduces the Deep Gaussian Process Sampling (DGPS) algorithm for learning DGPs. The DGPS algorithm relies on Monte Carlo sampling to circumvent the intractability of exact inference in DGPs. It is described as being more straightforward than existing DGP methods and able to more easily adapt to using arbitrary kernels. The document provides background on Gaussian processes and motivation for using deep Gaussian processes before describing the DGPS algorithm in more detail.
This document is a model exam paper for a data communication and networks course. It contains 5 questions testing knowledge of:
1) The OSI model layers and protocols, differences between IPv4 and IPv6.
2) Encoding techniques used in data transmission including Manchester encoding and modulation methods.
3) How data is broken into frames for transmission and the selective reject ARQ protocol.
4) Using time division multiplexing and frequency division multiplexing to transmit multiple telephone channels.
5) Differences between circuit switching and packet switching, comparing OSI layer 2 to IEEE 802 layer 2, functions of hubs and switches, transmission media for Ethernet, and Fast Ethernet specifications.
This document contains 16 questions about operating systems topics like multiprocessing, scheduling algorithms, memory management, paging, and file systems. The questions cover symmetric and asymmetric multiprocessing, problems with serial processing, I/O methods, storage hierarchy caching, process control blocks, microkernel benefits, scheduling algorithms like FCFS, priority and round robin, memory allocation schemes like best fit and worst fit, page replacement algorithms like FIFO and optimal, file system directory structures. Diagrams and calculations of metrics like waiting times are required in some questions.
2.5D Clip-Surfaces for Technical VisualizationMatthias Trapp
The concept of clipping planes is well known in computer graphics and can be used to create cut-away views. But clipping against just analytical defined planes is not always suitable for communicating every aspect of such visualization. For example, in hand-drawn technical illustrations, artists tend to communicate the difference between a cut and a model feature by using non-regular, sketchy cut lines instead of straight ones. To enable this functionality in computer graphics, this paper presents a technique for applying 2.5D clip-surfaces in real-time. Therefore, the clip plane equation is extended with an additional offset map, which can be represented by a texture map that contains height values. Clipping is then performed by varying the clip plane equation with respect to such an offset map. Further, a capping technique is proposed that enables the rendering of caps onto the clipped area to convey the impression of solid material. It avoids a re-meshing of a solid polygonal mesh after clipping is performed. Our approach is pixel precise, applicable in real-time, and takes fully advantage of graphics accelerators.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.
The document discusses computing the longest common prefix array (LCP array) directly from the Burrows-Wheeler transform (BWT) of a string, rather than first constructing the suffix array. It presents the first algorithm that computes the LCP array in linear time O(n) directly from the wavelet tree of the BWT-transformed string, requiring approximately 2.2n bytes of space. This is more efficient than previous approaches that first constructed the suffix array and then derived the LCP array, requiring at least 5n bytes of space.
This document discusses modifications to B-spline interpolation formulations. It begins by describing B-splines and their use in interpolation. It notes that constant, linear, quadratic, and cubic interpolation correspond to B-spline orders of 1, 2, 3, and 4, respectively. It then presents a modified relation where a shifted B-spline is used, with the shift determined by the interpolation order. For example, with linear interpolation the shift is 1. Graphs demonstrate how this modified relation gives nearer points higher weight. The document concludes by discussing constant interpolation in 2D images.
A New Chaos Based Image Encryption and Decryption using a Hash FunctionIRJET Journal
This document proposes a new chaos-based image encryption and decryption scheme using Arnold's cat map for pixel permutation and the Lorenz system for diffusion. A hash function, specifically MurmurHash3, is used to generate the permutation and diffusion keys. This helps accelerate the diffusion process and reduces the number of cipher cycles needed compared to previous schemes. The encryption process involves first permuting the pixel positions using the cat map, with control parameters determined by the hash value of the original image. Then diffusion is performed using the Lorenz system to generate the keystream. Decryption follows the reverse process using the same keys. Security analysis demonstrates the scheme has a large key space and the encrypted images pass various statistical tests, indicating the
This document discusses data compression techniques including lossless compression methods like run-length encoding and statistical compression, as well as lossy compression methods like JPEG and MPEG. It also covers recent developments in data compression technology and the use of neural networks for image compression.
The document discusses improved approaches to implementing dynamic tries in a space-efficient manner. It summarizes the Bonsai data structure, which supports dynamic trie operations in O(1) expected time but uses O(nlogσ + nloglogn) bits of space. The document then proposes a new approach called m-Bonsai that uses only O(nlogσ) bits of space in expectation while also supporting O(1) expected time operations, achieving the optimal space bound. Experimental results show m-Bonsai uses significantly less memory than Bonsai and has comparable or better performance.
The Journal of MC Square Scientific Research is published by MC Square Publication on the monthly basis. It aims to publish original research papers devoted to wide areas in various disciplines of science and engineering and their applications in industry. This journal is basically devoted to interdisciplinary research in Science, Engineering and Technology, which can improve the technology being used in industry. The real-life problems involve multi-disciplinary knowledge, and thus strong inter-disciplinary approach is the need of the research.
The document describes a data structure called a Compact Dynamic Rewritable Array (CDRW) that compactly stores arrays where each entry can be dynamically rewritten. It supports creating an array of size N where each entry is initially 0 bits, setting an entry to a value of at most k bits, and getting an entry's value. The goal is to use close to the minimum possible space of the sum of each entry's length while supporting these operations in O(1) time. The document presents solutions using compact hashing that achieve O(1) time for get and set using (1+) times the minimum space plus O(N) bits, for any constant >0. Experimental results show these perform well in terms
High-quality rendering of 3D virtual environments typically depends on high-quality 3D models with significant geometric complexity and texture data. One major bottleneck for real-time image-synthesis represents the number of state changes, which a specific rendering API has to perform. To improve performance, batching can be used to group and sort geometric primitives into batches to reduce the number of required state changes, whereas the size of the batches determines the number of required draw-calls, and therefore, is critical for rendering performance. For example, in the case of texture atlases, which provide an approach for efficient texture management, the batch size is limited by the efficiency of the texture-packing algorithm and the texture resolution itself. This paper presents a pre-processing approach and rendering technique that overcomes these limitations by further grouping textures or texture atlases and thus enables the creation of larger geometry batches. It is based on texture arrays in combination with an additional indexing schema that is evaluated at run-time using shader programs. This type of texture management is especially suitable for real-time rendering of large-scale texture-rich 3D virtual environments, such as virtual city and landscape models.
A Three-Point Directional Search Block Matching Algorithm Yayah Zakaria
This paper proposes compact directional asymmetric search patterns, which we have named as three-point directional search (TDS). In most fast search motion estimation algorithms, a symmetric search pattern is usually set at the minimum block distortion point at each step of the search. The design of the
symmetrical pattern in these algorithms relies primarily on the assumption that the direction of convergence is equally alike in each direction with respect to the search center. Therefore, the monotonic property of real -world video sequences is not properly used by these algorithms. The strategy of TDS is to keep searching for the minimum block distortion point in the most probable directions, unlike the previous fast search motion estimation algorithms where all the directions are checked. Therefore, the proposed method significantly reduces the number of search points for locating a motion vector. Compared to conventional fast algorithms, the proposed method has the fastest search speed and most satisfactory PSNR values for
all test sequences.
The researcher focuses on studying fundamental tradeoffs between cache-obliviousness, cache-optimality, and parallelism of algorithms and data structures. Their work combines theory and experiments on topics like stencil computation, dynamic programming, and numerical algorithms. Recent work showed that optimal time and cache complexity can be achieved simultaneously for problems like longest common subsequence via a "cache-oblivious wavefront" scheduling technique. Open questions remain about applying this approach more broadly and understanding tradeoffs between time and cache complexity.
A fuzzy clustering algorithm for high dimensional streaming dataAlexander Decker
This document summarizes a research paper that proposes a new dimension-reduced weighted fuzzy clustering algorithm (sWFCM-HD) for high-dimensional streaming data. The algorithm can cluster datasets that have both high dimensionality and a streaming (continuously arriving) nature. It combines previous work on clustering algorithms for streaming data and high-dimensional data. The paper introduces the algorithm and compares it experimentally to show improvements in memory usage and runtime over other approaches for these types of datasets.
This document discusses the design of a pipelined architecture for sparse matrix-vector multiplication on an FPGA. It begins with introductions to matrices, linear algebra, and matrix multiplication. It then describes the objective of building a hardware processor to perform multiple arithmetic operations in parallel through pipelining. The document reviews literature on pipelined floating point units. It provides details on the proposed pipelined design for sparse matrix-vector multiplication, including storing vector values in on-chip memory and using multiple pipelines to complete results in parallel. Simulation results showing reduced power and execution time are presented before concluding the design can improve performance for scientific applications.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using clustering algorithms to construct ontologies from text documents. It begins with an introduction to semantic search, ontologies in the semantic web, and clustering. It then describes the ROCK clustering algorithm in detail. The main tasks to perform are preprocessing text documents, normalizing term weights, applying latent semantic indexing via singular value decomposition, and using the ROCK clustering algorithm. The goal is to group similar documents into clusters to help construct an ontology from the unstructured text data.
This document presents and compares three approximation methods for thin plate spline mappings that reduce the computational complexity from O(p3) to O(m3), where m is a small subset of points p. Method 1 uses only the subset of points to estimate the mapping. Method 2 uses the subset of basis functions with all target values. Method 3 approximates the full matrix using the Nyström method. Experiments on synthetic grids show Method 3 has the lowest error, followed by Method 2, with Method 1 having the highest error. The three methods trade off accuracy, computation time, and the ability to do principal warp analysis.
New Chaotic Substation and Permutation Method for Image Encryptiontayseer Karam alshekly
New Chaotic Substation and Permutation Method for Image Encryption is introduced based on combination between Block Cipher and chaotic map. The new algorithm encrypts and decrypts a block of 500 byte. Each block is firstly permuted by using the hyper-chaotic map and then the result is substituted using 1D Bernoulli map. Finally the resulted block is XORed with the key block. The proposed cipher image subjected to number of tests which are the security analysis (key space analysis and key sensitivity analysis) and statistical attack analysis (histogram, correlation, and differential attack and information entropy) and all results show that the proposed encryption scheme is secure because of its large key space; it’s highly sensitivity to the cipher keys and plain-images.
Training and Inference for Deep Gaussian ProcessesKeyon Vafa
The document discusses training and inference for deep Gaussian processes (DGPs). It introduces the Deep Gaussian Process Sampling (DGPS) algorithm for learning DGPs. The DGPS algorithm relies on Monte Carlo sampling to circumvent the intractability of exact inference in DGPs. It is described as being more straightforward than existing DGP methods and able to more easily adapt to using arbitrary kernels. The document provides background on Gaussian processes and motivation for using deep Gaussian processes before describing the DGPS algorithm in more detail.
This document is a model exam paper for a data communication and networks course. It contains 5 questions testing knowledge of:
1) The OSI model layers and protocols, differences between IPv4 and IPv6.
2) Encoding techniques used in data transmission including Manchester encoding and modulation methods.
3) How data is broken into frames for transmission and the selective reject ARQ protocol.
4) Using time division multiplexing and frequency division multiplexing to transmit multiple telephone channels.
5) Differences between circuit switching and packet switching, comparing OSI layer 2 to IEEE 802 layer 2, functions of hubs and switches, transmission media for Ethernet, and Fast Ethernet specifications.
This document contains 16 questions about operating systems topics like multiprocessing, scheduling algorithms, memory management, paging, and file systems. The questions cover symmetric and asymmetric multiprocessing, problems with serial processing, I/O methods, storage hierarchy caching, process control blocks, microkernel benefits, scheduling algorithms like FCFS, priority and round robin, memory allocation schemes like best fit and worst fit, page replacement algorithms like FIFO and optimal, file system directory structures. Diagrams and calculations of metrics like waiting times are required in some questions.
2.5D Clip-Surfaces for Technical VisualizationMatthias Trapp
The concept of clipping planes is well known in computer graphics and can be used to create cut-away views. But clipping against just analytical defined planes is not always suitable for communicating every aspect of such visualization. For example, in hand-drawn technical illustrations, artists tend to communicate the difference between a cut and a model feature by using non-regular, sketchy cut lines instead of straight ones. To enable this functionality in computer graphics, this paper presents a technique for applying 2.5D clip-surfaces in real-time. Therefore, the clip plane equation is extended with an additional offset map, which can be represented by a texture map that contains height values. Clipping is then performed by varying the clip plane equation with respect to such an offset map. Further, a capping technique is proposed that enables the rendering of caps onto the clipped area to convey the impression of solid material. It avoids a re-meshing of a solid polygonal mesh after clipping is performed. Our approach is pixel precise, applicable in real-time, and takes fully advantage of graphics accelerators.
COLOR IMAGE ENCRYPTION BASED ON MULTIPLE CHAOTIC SYSTEMSIJNSA Journal
This document proposes a novel color image encryption scheme based on multiple chaotic systems. The scheme utilizes the ergodic properties of chaotic systems to perform pixel permutation and applies a substitution operation to achieve diffusion. In the permutation stage, two generalized Arnold maps are used to generate hybrid chaotic sequences to permute pixel positions. In the diffusion stage, four pseudo-random gray value sequences generated by another generalized Arnold map are used to diffuse the permuted image via bitwise XOR operations. Security analysis shows the scheme has a large key space and is highly secure against statistical attacks, differential attacks, and chosen/known plaintext attacks.
The document discusses computing the longest common prefix array (LCP array) directly from the Burrows-Wheeler transform (BWT) of a string, rather than first constructing the suffix array. It presents the first algorithm that computes the LCP array in linear time O(n) directly from the wavelet tree of the BWT-transformed string, requiring approximately 2.2n bytes of space. This is more efficient than previous approaches that first constructed the suffix array and then derived the LCP array, requiring at least 5n bytes of space.
This document discusses modifications to B-spline interpolation formulations. It begins by describing B-splines and their use in interpolation. It notes that constant, linear, quadratic, and cubic interpolation correspond to B-spline orders of 1, 2, 3, and 4, respectively. It then presents a modified relation where a shifted B-spline is used, with the shift determined by the interpolation order. For example, with linear interpolation the shift is 1. Graphs demonstrate how this modified relation gives nearer points higher weight. The document concludes by discussing constant interpolation in 2D images.
A New Chaos Based Image Encryption and Decryption using a Hash FunctionIRJET Journal
This document proposes a new chaos-based image encryption and decryption scheme using Arnold's cat map for pixel permutation and the Lorenz system for diffusion. A hash function, specifically MurmurHash3, is used to generate the permutation and diffusion keys. This helps accelerate the diffusion process and reduces the number of cipher cycles needed compared to previous schemes. The encryption process involves first permuting the pixel positions using the cat map, with control parameters determined by the hash value of the original image. Then diffusion is performed using the Lorenz system to generate the keystream. Decryption follows the reverse process using the same keys. Security analysis demonstrates the scheme has a large key space and the encrypted images pass various statistical tests, indicating the
This document discusses data compression techniques including lossless compression methods like run-length encoding and statistical compression, as well as lossy compression methods like JPEG and MPEG. It also covers recent developments in data compression technology and the use of neural networks for image compression.
The document discusses improved approaches to implementing dynamic tries in a space-efficient manner. It summarizes the Bonsai data structure, which supports dynamic trie operations in O(1) expected time but uses O(nlogσ + nloglogn) bits of space. The document then proposes a new approach called m-Bonsai that uses only O(nlogσ) bits of space in expectation while also supporting O(1) expected time operations, achieving the optimal space bound. Experimental results show m-Bonsai uses significantly less memory than Bonsai and has comparable or better performance.
The Journal of MC Square Scientific Research is published by MC Square Publication on the monthly basis. It aims to publish original research papers devoted to wide areas in various disciplines of science and engineering and their applications in industry. This journal is basically devoted to interdisciplinary research in Science, Engineering and Technology, which can improve the technology being used in industry. The real-life problems involve multi-disciplinary knowledge, and thus strong inter-disciplinary approach is the need of the research.
The document describes a data structure called a Compact Dynamic Rewritable Array (CDRW) that compactly stores arrays where each entry can be dynamically rewritten. It supports creating an array of size N where each entry is initially 0 bits, setting an entry to a value of at most k bits, and getting an entry's value. The goal is to use close to the minimum possible space of the sum of each entry's length while supporting these operations in O(1) time. The document presents solutions using compact hashing that achieve O(1) time for get and set using (1+) times the minimum space plus O(N) bits, for any constant >0. Experimental results show these perform well in terms
High-quality rendering of 3D virtual environments typically depends on high-quality 3D models with significant geometric complexity and texture data. One major bottleneck for real-time image-synthesis represents the number of state changes, which a specific rendering API has to perform. To improve performance, batching can be used to group and sort geometric primitives into batches to reduce the number of required state changes, whereas the size of the batches determines the number of required draw-calls, and therefore, is critical for rendering performance. For example, in the case of texture atlases, which provide an approach for efficient texture management, the batch size is limited by the efficiency of the texture-packing algorithm and the texture resolution itself. This paper presents a pre-processing approach and rendering technique that overcomes these limitations by further grouping textures or texture atlases and thus enables the creation of larger geometry batches. It is based on texture arrays in combination with an additional indexing schema that is evaluated at run-time using shader programs. This type of texture management is especially suitable for real-time rendering of large-scale texture-rich 3D virtual environments, such as virtual city and landscape models.
A Three-Point Directional Search Block Matching Algorithm Yayah Zakaria
This paper proposes compact directional asymmetric search patterns, which we have named as three-point directional search (TDS). In most fast search motion estimation algorithms, a symmetric search pattern is usually set at the minimum block distortion point at each step of the search. The design of the
symmetrical pattern in these algorithms relies primarily on the assumption that the direction of convergence is equally alike in each direction with respect to the search center. Therefore, the monotonic property of real -world video sequences is not properly used by these algorithms. The strategy of TDS is to keep searching for the minimum block distortion point in the most probable directions, unlike the previous fast search motion estimation algorithms where all the directions are checked. Therefore, the proposed method significantly reduces the number of search points for locating a motion vector. Compared to conventional fast algorithms, the proposed method has the fastest search speed and most satisfactory PSNR values for
all test sequences.
The researcher focuses on studying fundamental tradeoffs between cache-obliviousness, cache-optimality, and parallelism of algorithms and data structures. Their work combines theory and experiments on topics like stencil computation, dynamic programming, and numerical algorithms. Recent work showed that optimal time and cache complexity can be achieved simultaneously for problems like longest common subsequence via a "cache-oblivious wavefront" scheduling technique. Open questions remain about applying this approach more broadly and understanding tradeoffs between time and cache complexity.
A fuzzy clustering algorithm for high dimensional streaming dataAlexander Decker
This document summarizes a research paper that proposes a new dimension-reduced weighted fuzzy clustering algorithm (sWFCM-HD) for high-dimensional streaming data. The algorithm can cluster datasets that have both high dimensionality and a streaming (continuously arriving) nature. It combines previous work on clustering algorithms for streaming data and high-dimensional data. The paper introduces the algorithm and compares it experimentally to show improvements in memory usage and runtime over other approaches for these types of datasets.
This document discusses the design of a pipelined architecture for sparse matrix-vector multiplication on an FPGA. It begins with introductions to matrices, linear algebra, and matrix multiplication. It then describes the objective of building a hardware processor to perform multiple arithmetic operations in parallel through pipelining. The document reviews literature on pipelined floating point units. It provides details on the proposed pipelined design for sparse matrix-vector multiplication, including storing vector values in on-chip memory and using multiple pipelines to complete results in parallel. Simulation results showing reduced power and execution time are presented before concluding the design can improve performance for scientific applications.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
A general weighted_fuzzy_clustering_algorithmTA Minh Thuy
This document proposes a framework for adapting iterative clustering algorithms to handle streaming data. The key ideas are:
1) As data arrives in chunks, cluster each chunk and represent the clustering results as a set of weighted centroids, with the weights indicating the number of data points assigned to each cluster.
2) Add the weighted centroids from previous chunks to the current chunk as it is clustered. This allows the algorithm to incorporate historical information from all previously seen data.
3) The weighted centroids produced by clustering the entire stream can then be used to assign labels or groups to new data points.
Experimental results on a large dataset treated as a stream show the streaming algorithm produces clusters almost identical to clustering all data at once
Parallel Batch-Dynamic Graphs: Algorithms and Lower BoundsSubhajit Sahu
Highlighted notes on Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds.
While doing research work under Prof. Kishore Kothapalli.
Laxman Dhulipala, David Durfee, Janardhan Kulkarni, Richard Peng, Saurabh Sawlani, Xiaorui Sun:
Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds. SODA 2020: 1300-1319
In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In this setting, the graph is stored on a number of machines, each having space strongly sublinear with respect to the number of vertices, that is, n for some constant 0 < < 1. Our goal is to handle batches of updates and queries where the data for each batch fits onto one machine in constant rounds of parallel computation, as well as to reduce the total communication between the machines. This objective corresponds to the gradual buildup of databases over time, while the goal of obtaining constant rounds of communication for problems in the static setting has been elusive for problems as simple as undirected graph connectivity. We give an algorithm for dynamic graph connectivity in this setting with constant communication rounds and communication cost almost linear in terms of the batch size. Our techniques combine a new graph contraction technique, an independent random sample extractor from correlated samples, as well as distributed data structures supporting parallel updates and queries in batches. We also illustrate the power of dynamic algorithms in the MPC model by showing that the batched version of the adaptive connectivity problem is P-complete in the centralized setting, but sub-linear sized batches can be handled in a constant number of rounds. Due to the wide applicability of our approaches, we believe it represents a practically-motivated workaround to the current difficulties in designing more efficient massively parallel static graph algorithms.
Parallel Batch-Dynamic Graphs: Algorithms and Lower BoundsSubhajit Sahu
In this paper we study the problem of dynamically
maintaining graph properties under batches of edge
insertions and deletions in the massively parallel model
of computation. In this setting, the graph is stored
on a number of machines, each having space strongly
sublinear with respect to the number of vertices, that
is, n
for some constant 0 < < 1. Our goal is to
handle batches of updates and queries where the data
for each batch fits onto one machine in constant rounds
of parallel computation, as well as to reduce the total
communication between the machines. This objective
corresponds to the gradual buildup of databases over
time, while the goal of obtaining constant rounds of
communication for problems in the static setting has
been elusive for problems as simple as undirected graph
connectivity.
We give an algorithm for dynamic graph connectivity
in this setting with constant communication rounds and
communication cost almost linear in terms of the batch
size. Our techniques combine a new graph contraction
technique, an independent random sample extractor from
correlated samples, as well as distributed data structures
supporting parallel updates and queries in batches.
We also illustrate the power of dynamic algorithms in
the MPC model by showing that the batched version
of the adaptive connectivity problem is P-complete in
the centralized setting, but sub-linear sized batches can
be handled in a constant number of rounds. Due to
the wide applicability of our approaches, we believe
it represents a practically-motivated workaround to the
current difficulties in designing more efficient massively
parallel static graph algorithms.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
1) The document discusses mining data streams using an improved version of McDiarmid's bound. It aims to enhance the bounds obtained by McDiarmid's tree algorithm and improve processing efficiency.
2) Traditional data mining techniques cannot be directly applied to data streams due to their continuous, rapid arrival. The document proposes using Gaussian approximations to McDiarmid's bounds to reduce the size of training samples needed for split criteria selection.
3) It describes Hoeffding's inequality, which is commonly used but not sufficient for data streams. The document argues that McDiarmid's inequality, used appropriately, provides a more efficient technique for high-speed, time-changing data streams.
The document discusses porting a seismic inversion code to run in parallel using standard message passing libraries. It describes three options considered for distributing the large 3D seismic data across processors: mapping the data to a processor grid, treating it as a sparse matrix problem, or distributing the data as 1D vectors assigned to each processor. The third option was chosen as it best preserved the code structure, had regular dependencies, and simplified communications. The parallel code was implemented using the Distributed Data Library (DDL) for data management and the Message Passing Interface (MPI) for basic point-to-point communication between processors. Initial tests on clusters showed near linear speedup on up to 30 processors.
My name is Rose Tom. I am associated with statisticsassignmenthelp.com for the past 8 years and have been helping statistics students with their MyStataLab assignments.
I have a master's in Professional Statistics from Princeton University, USA.
The document summarizes research on distributing the computation of a genetic algorithm's fitness function to parallelize concept location. Four distributed architectures - simple client-server, database, hash-database, and hash configurations - were developed, tested, and compared. The experiments found that a simple architecture where each server tracked its own computations without data sharing had the fastest execution time, reducing the genetic algorithm computation by around 140 times compared to a single-machine implementation. Future work aims to experiment with different communication protocols and synchronization strategies on additional traces from other systems.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document summarizes a research paper that proposes a new heuristic called PAUSE for investigating the producer-consumer problem in distributed systems. The paper motivates the need to study this problem, describes PAUSE's approach of using compact configurations and decentralized components, outlines its implementation in Lisp and Java, and presents experimental results showing PAUSE outperforms previous methods. Related work investigating similar challenges is also discussed.
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETScsandit
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
DECISION TREE CLUSTERING: A COLUMNSTORES TUPLE RECONSTRUCTIONcscpconf
Column-Stores has gained market share due to promising physical storage alternative for analytical queries. However, for multi-attribute queries column-stores pays performance
penalties due to on-the-fly tuple reconstruction. This paper presents an adaptive approach for reducing tuple reconstruction time. Proposed approach exploits decision tree algorithm to
cluster attributes for each projection and also eliminates frequent database scanning.Experimentations with TPC-H data shows the effectiveness of proposed approach.
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
Similar to A Novel Approach of Caching Direct Mapping using Cubic Approach (20)
Brand Guideline of Bashundhara A4 Paper - 2024khabri85
It outlines the basic identity elements such as symbol, logotype, colors, and typefaces. It provides examples of applying the identity to materials like letterhead, business cards, reports, folders, and websites.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
A Novel Approach of Caching Direct Mapping using Cubic Approach
1. A Novel Approach Of Caching
Direct Mapping Using Cubic
Approach
Guidance
Professor Chandrasegar .T
Site,vit university Vellore
Team Member
Kamlesh Asati 11MCA0133
Aakansha Soni 11MCA0080
Harikesh Dwivedi 11MCA0083
04SETMCAO056
2. Aim
A novel approach of caching direct mapping
using cubic approach appreciate to improve
system efficiency without losses the data.
It means that decrease the data access time
with using sufficient memory but that’s not
enough while we are implement these things
with minimum time complexity and high
level security.
04SETMCAO056
3. Objective Scope
Increase the system performs.
Decrease the process execution time and find
the method in which time complexity in this
approach is minimum.
Try to produce accurate result without data
losses.
04SETMCAO056
4. Abstract
In the last few decades there is lot of research
works carried out for the improvement of c p u.
Caching address mapping technique plays a
vital role for the system improvement.
Based on the number of hits occurred on the
caching leads to the effective utilization of
processor.
in general when the caching memory size
increases then the number of page faults
/misses is going to get decreases between the
processor and the system memory.
In this concept the proposed work is to deal with
caching direct mapping and to secure the data in
a random and cubic based combinatorial
approach.
04SETMCAO056
5. Introduction
Cache Memory Mapping
The memory system has to quickly determine if a
given address is in the cache.
Caching address mapping techniques plays a vital
role for the system improvement. Based on the no
of hits occurred on the caching leads to the
effective utilization of processor.
There are three popular methods
1) Fully associative mapping
2) Set associative mapping
3) Direct mapping
04SETMCAO056
6. Direct Mapping
This is the most popular technique for
cache memory mapping.
If each block from main memory has only
one place it can appear in the cache, the
cache is said to be direct mapped.
Since a data block from ram can only be on
space line in the cache , it must always
replace the one block that was already
there.
04SETMCAO056
7. Address Splits into two part address
S w
Least significant w bits identify unique word
in block.
Most significant s bits specify one memory
block.
s bits divided into two parts tag line
s
a) Cache line address field r bits.
b) Tag field of s-r most significant bit.
S-r r
04SETMCAO056
8. Cubic Approach
In cubic approach for Mapping we map the
data in random and cubic base approach.
For this we use the formula of cubic equation.
F(x)= ax3+bx2+cx+d
04SETMCAO056
9. Existing System
In direct mapping, mapping is done with the linear approach.
For this we use hashing concept, If data is consist of n integers
and we have k number of cells then the address where in data
stored or retrieved is the ordinary hash function is Hash = n%k
H (k, i) = (h (k) + ci) modm
Think how linear mapping works then the answer is if the first
location is not free, then it will go to the next location and
check if the location is free or not, and so on until it finds
empty blocks or can’t find at all. For retrieving the data, hash
function and rehash function is used, retrieving is done by
using the hash function to find the key and check if the data
would coincide to the data needed. If not then rehash would
be needed. Until such time the correct location is found or an
empty space is encountered which shows the data does not
exists. In linear approach the searching takes so much time
because in linear mapping it searches in all blocks of memory
therefore the speed is slow.
04SETMCAO056
10. Literature Review
According to Zhenghong Wang and Ruby B. Lee ,
Caches ideally should have low miss rates and short
access times, and should be power efficient to system
at the same time.
Sincerely finding on efficient hits based on
information losses in caches have also moves the
security issue for it. finally this concept shows that
the cache architecture has low Miss Rates
comparable to a highly associative cache and short
access times and power efficiency close to that of a
direct-mapped cache.
Additional benefits that the include cache
architecture can bring, like fault method and hot-spot
revolution techniques.
04SETMCAO056
11. According to Mark D. Hill , cache is used to
improve the system cost, which have large
memory but using the cache memory we
increase the access time of the system.
Cache is small in size so it holds some data at a
time .cache memory hold that data which are
frequently used by the processor. For storing
cache retained the recent referenced
information.
Caches are successful due to temporal and
spatial locality. Temporal locality means future
references are likely to be made to the same
locations as recent references, while spatial
locality suggests that future references are also
likely to be made to locations near recent
references.
Caches take advantage of temporal locality by
retaining recently referenced Information.
04SETMCAO056
12. Methodology
Now we are trying to use our cubic approach to
map the memory. The family of cubic equation is
f(x) =ax3+bx2+cx+d.We use the function
h(x) = (ax 3+bx 2+cx+d) mod y
For finding the key element in cache memory .in
this formula we take the value of x between 0 to 15
and map the memory into cache between 0 to 15.
For finding the solution we gives the value of x
from 0 to 15 and change the values of a, b, c, d and
calculate the function value y= ax3+bx2+cx+d and
for the value of key element we calculate the value
y modmheremis 16.
04SETMCAO056
13. In this table we
observe this cubic
formula
F(x)=(ax3+bx2+cx+)
mody gives the
value which is
repeated in the
column (ymod16),it
means for that
value of a,b,c,d
it maps some
memory address
which is same.so
this is not a
correct mapping.
X a b c d y Mod y
0 2 3 3 1 1 1
1 2 3 3 1 9 9
2 2 3 3 1 35 3
3 2 3 3 1 91 11
4 2 3 3 1 189 13
5 2 3 3 1 341 5
6 2 3 3 1 559 15
7 2 3 3 1 855 7
8 2 3 3 1 1241 9
9 2 3 3 1 1729 1
10 2 3 3 1 2331 11
11 2 3 3 1 3069 3
12 2 3 3 1 3925 5
13 2 3 3 1 4941 13
14 2 3 3 1 6119 7
04SETMCA56 15 2 3 3 1 7471 15
14. State diagram
Start
0,2,4,6,8 0,2,4,6,8 1,3,5,7,9
1,3,5,7,9
1,3,5,7,9 0,2,4,6,8
0 to 9
a
Final/
hit
b
miss
c d
04SETMCAO056
15. We notice that the function
F(x)= (ax3+bx2+cx+d)mod y
give unique value
when we choose the value of a b & c such
that a & b must be even and c must be odd
but d consist any value between 0 to 9.
above given state diagram shows how the
function will work.
04SETMCAO056
16. Result
Using this approach we decrease the time
complexity of the direct mapping which is
good and complexity is o(n) , its also help the
decrease the process execution time.
input(0-15) output(0-15)
The proposed work is to deal with caching and
to secure the data in a random and cubic base
approach.
04SETMCAO056
Cubic remapping
system
f(x) =ax3+bx2+cx+d
17. So we are using the cubic approach which
maps the function more speedily than linear.
In linear approach power consumption is also
high using this approach we can also reduce
the power consumption.
Using cubic approach we get the time
complexity is o(n) .
04SETMCAO056
19. Conclusion
A cache is to improve system cost performance by
providing the capacity of the large, slow memory
with an access time close to that of the small, fast
cache.
This simple approach implements a form of by
passing the different address value.
We develop a cubic approach to evaluate the
performance of the proposed direct mapped caching
algorithm our method enhances the reuse behavior of
the temporal data while improving the reuse of the no
temporal data by cubic associativity.
From our experimental results, we see that our
method is both fast and accurate.
04SETMCAO056
20. Future Enhancement
Future reaches issue can give better solution
for this system , provided it overcomes the
problems like replacement strategy that can
better adopt to user access pattern and improve
the cache space utilization , security issue.
04SETMCAO056