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OCEANS Conference

作者： Cimiega, Thomas Badri-Hoeher, Sabah Univ Appl Sci Kiel Fac Comp Sci & Elect Engn Kiel Germany

ISBN: (纸本)9780692935590

In this work a navigational concept for a heterogeneous swarm of AUVs is treated. Inspired by search and rescue manoeuvres of emergency forces, the swarm consists of two types of agents that detect and inspect possible objects of interest like contaminations of munition, crash sites or pipeline leaks. Each agent type differs by its degrees of freedom, environmental sensors, navigational devices and software defined role. The main part of this work is the obligatory and optimized information exchange between the agent types to localize and inspect objects of interest. To evaluate the concept, a simulation environment for autonomous agents using the proposed algorithms for target localization is realized. Since this work is a general representation of a powerful concept, we use two common feature extraction methods for information encoding. First, we use Ri-HOG because it is deterministic in parametrization and results. On the other hand, we use artificial neural networks since they provide excellent results for image-related tasks such as segmentation, reconstruction, and object classification. Both methods are evaluated for their ability to reference the same object in two images with short feature vectors. We then test the performance of the methods as well as the advantageous concept of heterogeneous swarm behavior in terms of mission time and power consumption using an underwater simulation that uses synthetic sonar image data.

关键词： multi-agent systems autonomous vehicles cooperative systems search methods object recognition neural networks acoustics compression algorithms

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SLAP-CC: Set-Level Adaptive Prefetching for Compressed Caches 40

SLAP-CC: Set-Level Adaptive Prefetching for Compressed Cache...

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IEEE 40th International Conference on Computer Design (ICCD)

作者： AlBarakat, Laith M. Gratz, Paul, V Jimenez, Daniel A. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Data prefetching and cache compression are well-studied techniques to reduce the impact of memory latency. Data prefetching predicts future memory accesses and prefills the cache with the corresponding memory blocks in advance of explicit demands. Cache compression tries to increase the effective capacity of the cache with minimum area overhead. As we show, however, naively integrating the two techniques does not yield additive gains. We show that the extra ways per set that cache compression provides generally produce fewer hits than the ways in the baseline uncompressed cache. Hence, prefetching more aggressively to those sets with compression-added ways and more conservatively to those sets with less compression provides substantial benefit. In this paper we present set-level adaptive prefetching for compressed caches (SLAP-CC), a compressibility aware prefetching technique that adapts prefetch aggressiveness to the workload compressibility to maximize prefetch coverage. SLAP-CC dynamically adjusts the prefetch confidence threshold on a per-set basis, based on the set's compression. SLAP-CC achieves a geometric mean speedup of 18.0% over a baseline system with compressed cache and no prefetching. Further, SLAP-CC significantly outperforms existing state of the art L2 prefetchers, SPP [1] and Best Offset [2] when combined with cache compression by an average of 7% and up to 25% on some workloads.

关键词： Additives Filtering Prefetching Data compression compression algorithms

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Interference Detection Using Time-Frequency Binary Hypothesis Testing 34

Interference Detection Using Time-Frequency Binary Hypothesi...

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34th IEEE Annual Military Communications Conference (MILCOM) on Leveraging Technology - The Joint Imperative

作者： Marcum, Andrew C. Kim, Joon Young Love, David J. Krogmeier, James V. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA Raytheon Co Space & Airborne Syst Ft Wayne IN 46808 USA

ISBN: (纸本)9781509000739

The growth in demand for wireless broadband is increasing the instrumental value of characterizing interference in spectral bands of interest. In particular, detection and classification of interference is an important function for mobile ad hoc network (MANET) nodes to optimize performance. In this paper, we propose interference detection and classification techniques for a network that consists of N distributed nodes and a classification center connected to each node by a robust link. Due to the limited capacity of the network connecting these nodes, each node compresses the observed signal into a binary vector that spans time and frequency and provides the bits to a classification center. In turn, the classification center stitches the compressed signals from all nodes into a single structure with characteristics that are exploited to detect and classify. The Kolmorgorov-Smirnov test is proposed for node compression. Furthermore, bounds on theoretical classification performance from finite blocklength coding theory are defined. Finally, we propose a minimum distance method to perform classification.

关键词： compression algorithms Detectors Interference Mobile ad hoc networks Signal processing Time-frequency analysis

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Reconfigurable high resolution network camera 11

Reconfigurable high resolution network camera

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11th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2003)

作者： Filippov, A Elphel Inc. United States

ISBN: (纸本)0769519792

We present the result of development of the reconfigurable high resolution network camera (Proakis and Manolakis, 1989). The design goal was not just to build an end user product, but also to create both flexible and compact development platform suitable for experimenting with image processing and compression algorithms. To make this possible, Elphel model 313 camera has embedded computer running free (GNU GPL) software over Linux operating system and general purpose reconfigurable FPGA (field programmable gate array) with the HDL code not bound by any commercial IP licensing. © 2003 IEEE.

关键词： Algorithm design and analysis Cameras compression algorithms Embedded computing Embedded software Field programmable gate arrays Hardware design languages Image processing Linux Operating systems

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DAC: Dedup-Assisted compression Scheme for Improving Lifetime of NAND Storage Systems 20

DAC: Dedup-Assisted Compression Scheme for Improving Lifetim...

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20th Conference and Exhibition on Design, Automation and Test in Europe (DATE)

作者： Park, Jisung Lee, Sungjin Kim, Jihong Seoul Natl Univ Seoul South Korea Inha Univ Incheon South Korea

ISBN: (纸本)9783981537093

Thanks to an aggressive scaling of semiconductor devices, the capacity of NAND flash-based solid-state-drives (SSDs) has increased greatly. However, this benefit comes at the expense of a serious degradation of NAND device's lifetime. In order to improve the lifetime of flash-based SSDs, various data reduction techniques, such as deduplication, lossless compression, and delta compression, are rapidly adopted to SSDs. Although each technique has been extensively studied, efficiently combining these techniques in a synergistic fashion was not thoroughly investigated. In this paper, we propose a novel dedup-assisted compression (DAC) scheme that integrates existing data reduction techniques so that potential benefits of individual ones can be maximized while overcoming their inherent limitations. By doing so, DAC greatly reduces the amount of write traffic sent to SSDs. DAC also requires negligible resources by utilizing existing hardware modules. Our evaluation results show that the proposed DAC decreases the amount of written data by up to 30% over a simple integration of deduplication and lossless compression.

关键词： Entropy Hardware Encoding Engines compression algorithms Pattern matching Data compression

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Activations Quantization for Compact Neural Networks 23

Activations Quantization for Compact Neural Networks

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23rd IEEE International Conference on Digital Signal Processing (DSP)

作者： Wei, Yadong Zhao, Zhixu Taoyang Zhen, Zishuo Hao, Zeyu Ren, Pengju Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538668122;9781538668115

As optimizing neural networks to improve processing speed has become a necessary process in industrial deployment, emerging challenges due to limited storage and computation when adopting neural networks on embedded system need to be addressed. There are some previous studies focusing on training neural networks with low precision weights and activations, like Binarized neural network and DoReFa-Net. Meanwhile, an alternative approach is deployed to reduce number of operations and model size, like MobileNet and SqueezeNet. In this work, by adopting SQ(Scale Quantization) method to quantify activations of each neuron, we have gained a more satisfying result especially in compact network models. For SqueezeDet on KITTI dataset, SQ achieves 22.86% more accuracy on means correct detections, while means location and classification error decrease by 41.84% and 68.57%, compared with DoReFa-Net with 2-bits activations. Additionally, we also propose the SQT(Scale Quantization with Threshold) method to further improve the quantization flexibility. It is worth mentioned that our approach is primarily designed for activations quantization, but it can also be complemented with other orthogonal methods to further achieving efficient neural network processing at the edge.

关键词： Biological neural networks Quantization (signal) compression algorithms Neurons Training Computational modeling

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A High Performance compression Method for Climate Data

A High Performance Compression Method for Climate Data

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Songbin Liu Xiaomeng Huang Yufang Ni Haohuan Fu Guangwen Yang Ministry of Education Key Laboratory for Earth System Modeling Tsinghua University Beijing China Joint Center for Global Change Studies Beijing China

Climate modeling data are usually multidimensional arrays of floating-point numbers. These arrays typically have two or three spatial dimensions and one temporal dimension, describing the evolvement of climate variables in a time span. With the advances of high performance computing, the volume of climate data is expanding exponentially, bringing tough challenges for climate data archiving and sharing. In this paper, we propose a lossless compression algorithm for the time-spatial climate floating-point arrays. Our compression algorithm can eliminate more data redundancy efficiently through adaptive prediction, XOR-differencing, and multi-way compression. In addition, static regions, which are very common in climate data, can be identified and compressed more efficiently. Moreover, to utilize the multi-cores on modern computers, we proposed a method to parallelize our compression algorithm. Evaluations demonstrate that single thread version of our compression method can achieve the best balance in compression ratios, deflating throughputs and inflating throughputs. And the parallel version can achieve 800 MB/s deflating throughputs and over 2600 MB/s inflating throughputs on a 16-core server.

关键词： Meteorology Compressors Throughput Correlation Data models Arrays compression algorithms

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Multi-stream word-based compression algorithm

Multi-stream word-based compression algorithm

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International Conference on Computer Science and Engineering (UBMK)

作者： Emir Öztürk Altan Mesut Banu Diri Trakya Universitesi Edirne TR Yildiz Teknik Universitesi Istanbul TR

In this article, we present a novel word-based lossless compression algorithm for text files which uses a semi-static model. We named our algorithm as Multi-stream Word-based compression Algorithm (MWCA), because it stores the compressed forms of the words in three individual streams depending on their frequencies in the text. It also stores two dictionaries and a bit vector as a side information. In our experiments MWCA obtains compression ratio over 3,23 bpc on average and 2,88 bpc on files larger than 50 MB. If a variable length encoder like Huffman Coding is used after MWCA, given ratios will reduce to 2,63 and 2,44 bpc respectively. With the advantage of its multi-stream structure MWCA could become a good solution especially for storing and searching big text data.

关键词： compression algorithms Art Dictionaries Huffman coding Jacobian matrices Markov processes

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Simultaneous Encryption/compression of Images Using Alpha Rooting

Simultaneous Encryption/Compression of Images Using Alpha Ro...

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Data compression Conference (DCC)

作者： Eric Wharton Karen Panetta Sos Agaian Department of Electrical and Computer Engineering Tufts University Medford MA USA College of Engineering University of Texas San Antonio San Antonio TX USA

Summary form only given. Significant work has been performed on encrypting images and compressing images as two separate problems, but traditional encryption techniques generally degrade the compression ratio. To circumvent these issues, two methods have been used. The first employs known encryption algorithms on compressed image data. The second develops compression algorithms which work well for encrypted data. The contribution of this paper is using alpha rooting to perform simultaneous compression and encryption. This achieves improved compression performance in terms of computational complexity and compression ratio. Results are shown for 2 of the well known benchmark images, using the well known JPEG image compression standard to demonstrate the effectiveness of alpha rooting for simultaneous encryption and compression.

关键词： Cryptography Image coding Degradation Transform coding Source coding Entropy Data compression Educational institutions compression algorithms Streaming media

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A novel algorithm for high compression rates focalized on electrical power quality signals

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HELIYON 2021年第3期7卷 e06475页

作者： Ruiz, Milton Simani, Silvio Inga, Esteban Jaramillo, Manuel Univ Politecn Salesiana Quito Ecuador BUnivers Ferrara Ferrara Italy

This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.

关键词： compression algorithms Data compression Data processing Power quality (PQ) Wavelet transforms

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