检索结果-内蒙古大学图书馆

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Manzhen Wang Yuanyong Luo Mengyu An Yuou Qiu Muhan Zheng Zhongfeng Wang Hongbing Pan School of Electronic Science and Engineering Nanjing University Nanjing 210023 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Approximate multipliers have recently attracted great attention due to their substantially lower energy consumption and area overhead. But previous approximate multiplier designs are mainly focused on the design of approximate compressors, little attention is paid to the compression strategy of partial product matrix. This paper proposes an optimized universal compression scheme for the compressor-based approximate multiplier. When we apply the new compression scheme to the state-of-the-art compressors, the accuracy of the approximate multiplier is largely increased and fewer exact adders are needed. To prove the efficiency of the new compression strategy, an 8-bit and a 12-bit approximate multipliers are designed using Verilog and synthesized under the TSMC 40-nm CMOS technology. Compared to the state-of-the-art, the experimental results indicate that the mean error distance of 8-bit multiplier decreases by 19.6%, with area and power reduced by 5.38% and 2.38% respectively; 12-bit multiplier has a reduction of 18.1% for mean error distance, with area and power reduced by 6.29% and 3.24% respectively. Moreover, application to image processing is presented, which shows that the proposed approximate multiplier has a better performance.

关键词： Compressors Image coding Adders Hardware Logic gates compression algorithms Erbium

来源：评论

学校读者我要写书评

暂无评论

Research and Application of K-means Clustering and DCT Double compression Algorithm in Vibration Sensor

Research and Application of K-means Clustering and DCT Doubl...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Youjun Han Yueming Hu Xuejie Huang Institute of industrial technology Guangzhou & Chinese academy of sciences Guangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China R/D/S Platform of LiB Manufacturing Institute of industrial technology Guangzhou & Chinese academy of sciences Guangzhou China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

In order to prolong the service life of wireless vibration sensors when collecting a large number of high-frequency vibration data, this paper studied the existing vibration data compression algorithms, put forward and analyzed the existing problems, and on this basis, proposed an effective mechanism of K-means clustering-discrete cosine transform (DCT) dual data compression. According to the characteristics of predictive maintenance data, K-means clustering-DCT dual compression algorithm firstly used the K-means algorithm to aggregate and classify vibration data. Then it carried out DCT compression according to the frequency domain characteristics of vibration signals. The verification results showed that the algorithm significantly improved data compression efficiency and reduced redundant data transmission by aggregating vibration data. Besides, under the same amount of data, the algorithm had better application performance after improving the peak signal-to-noise ratio compared with other algorithms.

关键词： Vibrations Clustering algorithms Data compression Prediction algorithms Classification algorithms Discrete cosine transforms compression algorithms

来源：评论

学校读者我要写书评

暂无评论

LIDAR Data compression Challenges and Difficulties 5

LIDAR Data Compression Challenges and Difficulties

引用

5th International Conference on Frontiers of Signal Processing (ICFSP)

作者： Abdelwahab, Mohamed M. El-Deeb, Walid S. Youssif, Aliaa. A. A. Zagazig Univ ECE Dept Fac Engn Zagazig Egypt AASTMT Coll Comp & Inf Technol Cairo Egypt

ISBN: (纸本)9781728152585

Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.

关键词： LIDAR compression algorithms lossless lossy 3D point cloud

来源：评论

学校读者我要写书评

暂无评论

Compressed Linear Algebra for Declarative Large-Scale Machine Learning

引用

COMMUNICATIONS OF THE ACM 2019年第5期62卷 83-91页

作者： Elgohary, Ahmed Boehm, Matthias Haas, Peter J. Reiss, Frederick R. Reinwald, Berthold Univ Maryland College Pk MD 20742 USA IBM Res Almaden San Jose CA USA Univ Massachusetts Amherst MA 01003 USA

Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to enable fast matrix-vector operations. General-purpose compression struggles to achieve both good compression ratios and fast decompression for block-wise uncompressed operations. Therefore, we introduce Compressed Linear Algebra (CIA) for lossless matrix compression. CIA encodes matrices with lightweight, value-based compression techniques and executes linear algebra operations directly on the compressed representations. We contribute effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Our experiments show good compression ratios and operations performance close to the uncompressed case, which enables fitting larger datasets into available memory. We thereby obtain significant end-to-end performance improvements.

关键词： compression ratio compression algorithms Access to Information Machine Learning COMPRESSED Artificial Intelligence operating performance Iterative Linear algebra information retrieval Surgery

来源：评论

学校读者我要写书评

暂无评论

An Online Trajectory compression Based on Retrace Point Detection

An Online Trajectory Compression Based on Retrace Point Dete...

引用

International Conference on Communication Technology (ICCT)

作者： Anbang Chen Linfeng Liu School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

In confronting plenty of trajectory records are generated and stored by GPS-equipped mobile devices making the device memory suffer a heavy storage pressure, it is a vital issue to compress the trajectories. This paper proposes a trajectory compression algorithm Velocity Preserving Trajectory compression based on Retrace Point Detection (VPTR). In VPTR, the retrace points and the velocity errors are taken to reflect the speeds and directions attached with the points. VPTR first determines whether the points to be retrace points on basis of the changed movement directions, and then the retrace points are extracted from the original trajectories. Especially, the retrace points are put in a buffer, and the sub-trajectories in the buffer are compressed according to the measured velocity errors. Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that VPTR can achieve a preferable trade-off among the compression error, compression ratio and running time.

关键词： Trajectory compression algorithms Euclidean distance Partitioning algorithms Measurement uncertainty Global Positioning System Velocity measurement

来源：评论

学校读者我要写书评

暂无评论

Lossless EEG compression Algorithm Based on Semi-Supervised Learning for VLSI Implementation

Lossless EEG Compression Algorithm Based on Semi-Supervised ...

引用

IEEE Asia-Pacific Conference on Circuits and Systems

作者： Yi-Hong Chen Yan-Ting Liu Tsun-Kuang Chi Chiung-An Chen Yih-Shyh Chiou Ting-Lan Lin Shih-Lun Chen Chung Yuan Christian University Taiwan Ming Chi University of Technology Taiwan National Taipei University of Technology Taipei City Taiwan

ISBN: (数字)9781728193960

ISBN: (纸本)9781728193977

In this paper, a hardware-oriented lossless EEG compression algorithm including a two-stage prediction, voting prediction and tri-entropy coding is proposed. In two stages prediction, 27 conditions and 6 functions are used to decide how to predict the current data from previous data. Then, voting prediction finds optimal function according to 27 conditions for best function to produce best Error (the difference of predicted data and current data). Moreover, a tri-entropy coding technique is developed based on normal distribution. The two-stage Huffman coding and Golomb-Rice coding was used to generate the binary code of Error value. In CHB-MIT Scalp EEG Database, the novel EEG compression algorithm achieves average compression rate to 2.37. The proposed hardware-oriented algorithm is suitable for VLSI implementation due to its low complexity.

关键词： Electroencephalography Encoding compression algorithms Wireless communication Prediction algorithms Huffman coding Databases

来源：评论

学校读者我要写书评

暂无评论

Bayesian Compressed Vector Autoregression for Financial Time-Series Analysis and Forecasting

引用

IEEE ACCESS 2019年 7卷 16777-16786页

作者： Taveeapiradeecharoen, Paponpat Chamnongthai, Kosin Aunsri, Nattapol Mae Fah Luang Univ Sch Management Chiang Rai 57100 Thailand King Mongkuts Univ Technol Thonburi Fac Engn Bangkok 10140 Thailand Mae Fah Luang Univ Sch Informat Technol Chiang Rai 57100 Thailand Mae Fah Luang Univ Brain Sci & Engn Innovat Res Grp Chiang Rai 57100 Thailand

Advanced time series models have been intensively developed and used to predict in financial data such as foreign exchange data (forex). In this paper, we implement the random compression method to reduce a large dimensional forex data into much smaller matrix form. Then, Bayesian inferences on vector autoregression are used to obtain all interesting parameters. Subsequently, the models are able to perform out-of-sample prediction up to 14 days ahead of forecast. For empirical works, 30 forex pairs are used in this paper. The results show that Bayesian compressed vector autoregression (BCVAR) and time-varying BCVAR (TVP-BCVAR) deliver excellent forecasting on AUD-JPY, CAD-CHF, CAD-JPY, EUR-DKK, EUR-MXN, and EUR-TRY forex datasets according to mean square forecasting error, outperforming the traditional benchmark Bayesian Autoregression.

关键词： Bayesian methods compression algorithms finance autoregressive processes forecasting Bayesian model averaging dymamic model averaging Kalman filter

来源：评论

学校读者我要写书评

暂无评论

Comments and Corrections Correction to “Data-Aware Adaptive Pruning Model compression Algorithm Based on a Group Attention Mechanism and Reinforcement Learning”

引用

IEEE Access 2022年 10卷 134769-134769页

作者： Zhi Yang Yuan Zhai Yi Xiang Jianquan Wu Jinliang Shi Ying Wu School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China

In the above article [1] , unfortunately, we had missed grant number CQYC20200309237 of the funding organization Chongqing Talent Plan. This affected only the detail of the funding organization and had no influence on the discussion and conclusions in the article. The complete and correct funding organizations with grant number are as follows.

关键词： Adaptation models Organizations Reinforcement learning compression algorithms

来源：评论

学校读者我要写书评

暂无评论

An Analytical Survey on Image compression

An Analytical Survey on Image Compression

引用

World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Pranali Dahiwal Anagha Kulkarni SPPU Pune India Cummins College of Engineering for Women Pune India

ISBN: (数字)9781728168234

ISBN: (纸本)9781728168241

There has been a significant increase in the number of smartphones and other devices that have been highly useful as a means of communication and entertainment. Therefore, there is a need to secure such devices as the majority of the smartphones that are being sold have been equipped with inbuilt biometric authentication system. Facial features are very diverse and have some kind of similarity with features in other people's faces, especially relatives and siblings. Therefore, there is a need to offload the facial authentication system and the recognition images to the cloud platform for computation. To this end, this research article has analyzed the past works based on the facial image compression paradigm for their improvements and limitations to assist in the implementation of a fast and effective facial image compression technique that will be discussed in detail in the upcoming publications.

关键词： Image coding Authentication Watermarking Face recognition Cloud computing compression algorithms Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Masked Time–Frequency Representation and Enhanced Priors Enabled Weak Electromechanical Signal Decomposition Algorithm

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ye Chao Tao Tao School of Mechanical Engineering Xi’an Jiaotong University Xi’an China Institute of Mechanical Manufacturing Technology China Academy of Engineering Physics Mianyang China

Electromechanical equipment typically consists of numerous parts, including motors, reducers, and loads, which contribute to the complexity of signals sampled from the equipment. Performing signal decomposition calculations to extract highly state-correlated yet weak components holds significant engineering application value, as these components are crucial for accurate equipment state analysis and evaluation. In this article, we propose a masked time–frequency representation (MTFR) enabled adaptive nonlinear chirp signal decomposition algorithm, abbreviated as MACMD. This algorithm primarily consists of three calculation steps, forming a multilayered framework for signal decomposition. The first step employs a novel method for signal preprocessing, which involves an MTFR to limit the calculation range of component signals (CMs), ensuring the extraction of significant and relevant components. The rest two steps, based on the original adaptive nonlinear chirp signal decomposition (ACMD) algorithm, incorporate smooth and sparse priors of the instantaneous frequency (IF) of the CM, which allows for more robust noise resistance and a more accurate representation of the extracted components. The constructed framework effectively integrates inner product-based algorithms, adaptive decomposition algorithms, and signal sparse prior information, achieving an effective deconstruction of the signal’s time–frequency structure. Furthermore, the decomposed CMs possess more significant frequencies/orders with physical meanings than the original signal, and the proposed algorithm provides an effective application tool for complex equipment condition monitoring. Finally, the effectiveness of the algorithm is validated through simulation and actual signal calculation.

关键词： Time-frequency analysis Signal processing algorithms Signal resolution Transforms Chirp Accuracy Matrix decomposition Data mining compression algorithms Polynomials

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：