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A real-time transmission system for digital video materials has been developed for remote editing or making digital archives through high speed networks. In this system a lossless codec has newly developed in which the motion comp...
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A real-time transmission system for digital video materials has been developed for remote editing or making digital archives through high speed networks. In this system a lossless codec has newly developed in which the motion compensated prediction coding method is utilized. This codec can he connected to PCI-bus of Workstation, so it becomes general and can he used for any high speed networks such as ATM, Gigabit-Ethernet or Fibre-channel. In this paper, the system configuration and specified lions of lossless CODEC are introduced and they are evaluated via computer simulation and real transmission experiments.
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摘要 :
A real-time transmission system for digital video materials has been developed for remote editing or making digital archives through high speed networks. In this system a lossless codec has newly developed in which the motion comp...
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A real-time transmission system for digital video materials has been developed for remote editing or making digital archives through high speed networks. In this system a lossless codec has newly developed in which the motion compensated prediction coding method is utilized. This codec can he connected to PCI-bus of Workstation, so it becomes general and can he used for any high speed networks such as ATM, Gigabit-Ethernet or Fibre-channel. In this paper, the system configuration and specified lions of lossless CODEC are introduced and they are evaluated via computer simulation and real transmission experiments.
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Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider ...
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Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider reconstruction after pruning in order to apply the network to actual devices. This study proposes a reconstruction process for channel-based network pruning. For lossless reconstruction, we focus on three components of the network: the residual block, skip connection, and convolution layer. Union operation and index alignment are applied to the residual block and skip connection, respectively. Furthermore, we reconstruct a compressed convolution layer by considering batch normalization. We apply our method to existing channel-based pruning methods for downstream tasks such as image classification, object detection, and semantic segmentation. Experimental results show that compressing a large model has a 1.93% higher accuracy in image classification, 2.2 higher mean Intersection over Union (mIoU) in semantic segmentation, and 0.054 higher mean Average Precision (mAP) in object detection than well-designed small models. Moreover, we demonstrate that our method can reduce the actual latency by 8.15× and 5.29× on Raspberry Pi and Jetson Nano, respectively.
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In this paper, real frequency design equations of narrowband impedance matching network with complex terminations are derived; which are used to design L, Pi and T type of networks. In the approach, there is no need to have termin...
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In this paper, real frequency design equations of narrowband impedance matching network with complex terminations are derived; which are used to design L, Pi and T type of networks. In the approach, there is no need to have termination models with component values, it is enough to have measurement values of termination impedances. A few examples are solved to exhibit the have measurement values of termination impedances. A few examples are solved to exhibit the merits and application of the derived equations.
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Compression algorithms are deeply used in Wireless Sensor Networks (WSNs) for data aggregation in order to reduce energy consumption and therefore increasing network lifetime. In this paper we compare several lossless compression ...
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Compression algorithms are deeply used in Wireless Sensor Networks (WSNs) for data aggregation in order to reduce energy consumption and therefore increasing network lifetime. In this paper we compare several lossless compression algorithms by means of real-world data. Moreover we present a simple and effective lossless compression algorithm that is able to outperform existing solutions and that, considering its inherent low complexity and memory requirements, is well suited for WSNs. (C) 2014 Elsevier Ltd. All rights reserved.
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In this paper, a broadband impedance matching network (equalizer) design algorithm has been proposed. In the equalizer, a lossless unsymmetrical lattice network has been utilized. The branch impedances of the lattice network are c...
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In this paper, a broadband impedance matching network (equalizer) design algorithm has been proposed. In the equalizer, a lossless unsymmetrical lattice network has been utilized. The branch impedances of the lattice network are considered as singly terminated lossless LC networks, since it is not desired to dissipate power in the equalizer. After giving the algorithm, its usage has been illustrated via an example.
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When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorit...
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When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the data to its minimum size. According to the Kolmogorov complexity, the compression performances of the algorithms implemented in the available compression programs in the system differ. Thus, it is impossible to deliberately select the best compression program before we try the compression operation. From this background, this paper proposes a method with a principal component analysis (PCA) and a deep neural network (DNN) to predict the entropy of data to be compressed. The method infers an appropriate compression program in the system for each data block of the input data and achieves a good compression ratio without trying to compress the entire amount of data at once. This paper especially focuses on lossless compression for image data, focusing on the image blocks. Through experimental evaluation, this paper shows the reasonable compression performance when the proposed method is applied rather than when a compression program randomly selected is applied to the entire dataset.
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In literature, synthesis of cascaded lossless commensurate lines have been realized via some iterative methods. So to be able to obtain the value of an element which is not the first one, the designer has to obtain all the values ...
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In literature, synthesis of cascaded lossless commensurate lines have been realized via some iterative methods. So to be able to obtain the value of an element which is not the first one, the designer has to obtain all the values of the elements connected before the desired one. But in this paper, explicit synthesis formulae of the networks containing cascaded lossless commensurate lines up to three have been derived analytically, and all the element values can be calculated independently.
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Datacenter networking is currently dominated by two major trends. One aims toward lossless, flat layer-2 fabrics based on Converged Enhanced Ethernet or InfiniBand, with benefits in efficiency and performance. The other targets fl...
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Datacenter networking is currently dominated by two major trends. One aims toward lossless, flat layer-2 fabrics based on Converged Enhanced Ethernet or InfiniBand, with benefits in efficiency and performance. The other targets flexibility based on Software Defined Networking, which enables Overlay Virtual Networking. Although clearly complementary, these trends also exhibit some conflicts: In contrast to physical fabrics, which avoid packet drops by means of flow control, practically all current virtual networks are lossy. We quantify these losses for several common combinations of hy-pervisors and virtual switches, and show their detrimental effect on application performance. Moreover, we propose a zero-loss Overlay Virtual Network (zOVN) designed to reduce the query and flow completion time of latency-sensitive datacenter applications. We describe its architecture and detail the design of its key component, the zVALE lossless virtual switch. As proof of concept, we implemented a zOVN prototype and benchmark it with Partition-Aggregate in two testbeds, achieving an up to 15-fold reduction of the mean completion time with three widespread TCP versions. For larger-scale validation and deeper introspection into zOVN, we developed an OMNeT++ model for accurate cross-layer simulations of a virtualized datacenter, which confirm the validity of our results.
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It is always preferable to use commercially available software tools to design broadband matching networks for microwave communication systems. However, for these tools, the matching network topology and element values must be sel...
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It is always preferable to use commercially available software tools to design broadband matching networks for microwave communication systems. However, for these tools, the matching network topology and element values must be selected properly. Therefore, in this paper, a practical method is presented to generate matching networks with good initial element values. Eventually, the performance of the designed matching network is optimized by employing the commercially available computer-aided design (CAD) tools. An example is given to illustrate the utilization of the proposed method. It is shown that the proposed method provides very good initials for CAD tools.
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