In this paper, an unstructured neuralnetwork based on the mathematics of holographic storage is presented. While the holographic process is analyzed by the distributed signal processing principles, the neuralnetwork...
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ISBN:
(纸本)3540362975
In this paper, an unstructured neuralnetwork based on the mathematics of holographic storage is presented. While the holographic process is analyzed by the distributed signal processing principles, the neuralnetwork architecture is adapted to the generalized support vector machine. This work is inspired by similarities between brain waves and the wave propagation and subsequent interference patterns seen in holograms. Then the mathematics to produce a general mathematical description of the holographic process is analyzed. From this analysis it is shown that how the holographic process can be used as an associative memory network. This aspect, makes this neuralnetwork formation process particularly useful for control.
This Volume 4555 of the conference proceedings contains 29 papers. Topics discussed include neuralnetwork and distributedprocessing, hardware parallel, character recognition, image segmentation and image classificat...
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This Volume 4555 of the conference proceedings contains 29 papers. Topics discussed include neuralnetwork and distributedprocessing, hardware parallel, character recognition, image segmentation and image classification.
The proceedings contains 55 papers from the 12th Euromicro conference on Parallel, distributed and network-Based processing PDP 2004. Topics discussed include: adaptive distributed execution of Java applications;dynam...
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ISBN:
(纸本)0769520839
The proceedings contains 55 papers from the 12th Euromicro conference on Parallel, distributed and network-Based processing PDP 2004. Topics discussed include: adaptive distributed execution of Java applications;dynamically scaling system area network;software design concepts of a distributed simulation kernel;creating scalable traffic simulation on clusters;remote management of distributed applications;abstracting the grid;adaptive task farm implementation strategies;making community work aware;improving cache locality with blocked array layouts;parallelization of a neural net training program in a grid environment and fast dependence analysis in a multimedia vectorizing compiler.
The proceedings contain 42 papers. The topics discussed include: an efficient compilation of coarse-grained reconfigurable architectures utilizing pre-optimized sub-graph mappings;evaluating micro-batch and data frequ...
ISBN:
(纸本)9781665469586
The proceedings contain 42 papers. The topics discussed include: an efficient compilation of coarse-grained reconfigurable architectures utilizing pre-optimized sub-graph mappings;evaluating micro-batch and data frequency for stream processing applications on multi-cores;a parallel approximation algorithm for the steiner forest problem;exploiting vector extensions to accelerate time series analysis;a neuralnetwork to estimate isolated performance from multi-program execution;a heuristic for constructing minimum average stretch spanning tree using betweenness centrality;accelerating distributed deep reinforcement learning by in-network experience sampling;parallel integer multiplication;advancing database system operators with near-data processing;and clustering datasets in cloud computing environment for user identification.
The paper proposes a formalization process of Big Data distributed intelligent processing using Cloud-Fog-Dew architecture. This process provides specialized services, based on continuous support of experts in areas o...
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In this paper, we describe possible applications of early exit deep neuralnetworks in magnetic resonance imaging, aiming to improve patient scan times and reduce processing costs. The solutions rely on deep neural ne...
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Privacy preservation is critical for neuralnetwork inference, which often involves collaborative execution of different parties to make predictions on sensitive data based on sensitive neuralnetwork models. However,...
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This paper presents a new approach to the hyper-distributed hyper-parallel implementation of the artificial intelligent (AI) heuristic algorithms for real-time searching, matching and planning. By using the competitiv...
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This paper presents a new approach to the hyper-distributed hyper-parallel implementation of the artificial intelligent (AI) heuristic algorithms for real-time searching, matching and planning. By using the competitive activation mechanism of dynamically clustering neuralnetworks, the concurrent propagations and competitions of concurrent autowaves yielded by distributed parallel heuristic AI algorithms for searching any implicit AND/OR graph are realized. Compared with the AI approaches based on the conventional sequential symbolic logic and the conventional neuralnetworks, the approach of this paper has many advantages in many respects, such as high processing speed, always successful obtainment of the optimal solution, local connections between cells, easy utilization of heuristic knowledge, and feasibility of the VLSI implementation.
For ECG signal processing, information extraction from a noisy background is the fundamental objective. Filtering (noise suppression, baseline wander elimination) is a very important step in efficient ECG signal featu...
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ISBN:
(纸本)142440360X
For ECG signal processing, information extraction from a noisy background is the fundamental objective. Filtering (noise suppression, baseline wander elimination) is a very important step in efficient ECG signal features extracting to enhance the performance of automatic detection and classification of different cardiac diseases. In this paper we used distributed Approximating Functional (DAF) wavelets to develop algorithms for signal approximation and filtering. These algorithms use Moving Average Artificial neuralnetwork with Wavelet type Hermite activating function. They are evaluated in MATLAB with signals from the MIT-BIH arrhythmia database and comparisons are made with the classical (radial basis function and sigmoid type activating function) artificial neuronal networks. New functions were created and integrated into MATLAB environment. The outcomes indicate a good performance tradeoff between accuracy and response time, making this type of algorithms desirable also for real-time implementation.
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