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An Optimized Rubber-Sheet algorithm for Continuous Area Cartograms

PROFESSIONAL GEOGRAPHER 2013年第1期65卷 16-30页

作者： Sun, Shipeng Univ Waterloo Sch Planning Fac Environm Waterloo ON N2L 3G1 Canada

This article optimizes a continuous area cartogram algorithm published in The Professional Geographer by Dougenik, Chrisman, and Niemeyer (DCN) in 1985. The DCN algorithm simulates a rubber sheet and is an iterative and approximate solution of cartogram construction. Although it remains popular because of its conceptual simplicity and cartographic quality, the DCN algorithm cannot completely preserve topology and its mathematical properties are inadequately explained. This article presents an optimization to the DCN algorithm, named Opti-DCN, with three improvements. First, it provides a mathematical condition for topology preservation. Second, new transformation equations that meet this condition are deduced from mathematics, which simultaneously optimize the global elasticity coefficient, a key parameter that greatly impacts the convergence rate of the rubber-sheet algorithm and the topological integrity of its generated cartograms. Last, the new algorithm simplifies the way of generating transforming forces in DCN and improves its efficiency of geometric transformation. Comparison shows that Opti-DCN is significantly faster to converge to equal-density cartograms and can mathematically and practically eliminate topological errors.

关键词： algorithm optimization continuous area cartogram rubber-sheet algorithm

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Y-Net: A Dual Path Model for High Accuracy Blind Source Separation

Y-Net: A Dual Path Model for High Accuracy Blind Source Sepa...

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Wu, Huanzhuo He, Jia Toemoeskoezi, Mate Fitzek, Frank H. P. Tech Univ Dresden Deutsch Telekom Chair Commun Networks Dresden Germany Ctr Tactile Internet Human In The Loop CeTI Dresden Germany

ISBN: (纸本)9781728173078

Industrial IoT (IIoT) in conjunction with UltraReliable Low-Latency Communications (URLLC) often struggles with data-rich, information-poor contexts. Blind Source Separation (BSS) is one of the key technologies which can obtain the desired high-value information from all of the observed raw sensory data. As shown by recent studies, BSS can be both fast enough for low-latency requirements and sufficiently accurate to be a reliable method in large IoT deployments. Nonetheless, the trade-off between signal context usage and data recovery accuracy often affects the separation quality of BSS. In this paper, we propose for the first time a novel dual path convolutional neural network model, called Y-Net, for high accuracy BSS. Specifically, the separation quality is improved by the parallel perception and joint combination of both high- and low-level features of input signals, which we demonstrated through extensive numerical evaluations. In particular, Y-Net improves the Source-to-Distortion Ratio by 2.70 % to 35.32 % for different target signals, while the model size is only slightly increased, compared to other current solutions.

关键词： blind source separation data analysis algorithm optimization neural network industry 4.0 URLLC

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Towards Fast and Efficient VVC Encoding 22

Towards Fast and Efficient VVC Encoding

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22nd IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Brandenburg, Jens Wieckowski, Adam Hinz, Tobias Henkel, Anastasia George, Valeri Zupancic, Ivan Stoffers, Christian Bross, Benjamin Schwarz, Heiko Marpe, Detlev Fraunhofer HHI Video Coding & Analyt Berlin Germany Free Univ Berlin Inst Comp Sci Berlin Germany

ISBN: (纸本)9781728193205

Versatile Video Coding (VVC) is a new international video coding standard to be finalized in July 2020. It is designed to provide around 50% bit-rate saving at the same subjective visual quality over its predecessor, High Efficiency Video Coding (H.265/HEVC). During the standard development, objective bit-rate savings of around 40% have been reported for the VVC reference software (VTM) compared to the HEVC reference software (HM). The unoptimized VTM encoder is around 9x, and the decoder around 2x, slower than HM. This paper discusses the VVC encoder complexity in terms of software runtime. The modular design of the standard allows a VVC encoder to trade off bit-rate savings and encoder runtime. Based on a detailed tradeoff analysis, results for different operating points are reported. Additionally, initial work on software and algorithm optimization is presented. With the optimized software algorithms, an operating point with an over 22x faster single-threaded encoder runtime than VTM can be achieved, i.e. around 2.5x faster than HM, while still providing more than 30% bit-rate savings over HM. Finally, our experiments demonstrate the flexibility of VVC and its potential for optimized software encoder implementations.

关键词： Versatile Video Coding Complexity algorithm optimization Video Encoder

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Movie Retrieval Based on Shazam algorithm 4

Movie Retrieval Based on Shazam Algorithm

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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Sun, Xiaoxue Zhang, Wenhui Chen, Deqin Commun Univ China Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538653739

With the development of multimedia and Internet technologies, the number of movie is growing at an unprecedented rate. How to quickly find the desired movie in a massive movie becomes more and more difficult. At present, most movie retrieval is still in the keyword search stage. This paper proposes a new method to retrieve the corresponding movie information based on movie audio. According to the characteristics of movie audio, this paper selects Shazam algorithm to optimize, which has good performance of the algorithm based on the audio retrieval algorithm in the current. After optimization, the average retrieval accuracy of the algorithm was improved by 0.82%, and it was applied to the mobile terminal. The experimental results proved that the actual retrieval effect was good and of great use value.

关键词： Shazam algorithm algorithm optimization movie retrieval

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Optimizing systemic risk through credit network reconstruction

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EMERGING MARKETS REVIEW 2023年 57卷

作者： Chao, Wang Jing, Ma Liu, Xiaoxing Nanjing Agr Univ Sch Finance Weigang 1 Nanjing 210095 Peoples R China Jiangsu Maritime Inst Sch Econ & Management Nanjing 211170 Peoples R China Southeast Univ Sch Econ & Management Sipailou 2 Nanjing 210096 Peoples R China

Capital buffers can improve the stability of the banking system, but they come at a cost. This study explores the efficient macro-prudential regulation of systemic risk from the perspective of capital network reconstruction. A model of option pricing is proposed to determine the market value of sector credits. Consequently, systemic risk is measured by both credit and interbank contagion risks. Credit network reconstruction is then optimized to minimize systemic risks. Our analysis was based on data from China's banking systems between 2008 and 2020. In different stress tests, the credit network reconstruction generally optimizes systemic risk to the lowest level. At the same time, it may save about 20% to 140% of the cost of capital. The optimization mechanism analysis shows that large banks with stability advantages share more shocks than small banks by reconstructing their sector credit network. As a result, the contagion process of systemic risk is prevented, and bankruptcy cascades are eliminated. These results imply that credit network reconstruction holds great potential for preventing systemic risks.

关键词： Systemic risks Credit network Capital requirement algorithm optimization

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Research on Correlation between Ubiquitous Telecommunication Data and Earthquake Intensity 4

Research on Correlation between Ubiquitous Telecommunication...

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4th International Conference on Electrical, Automation and Mechanical Engineering (EAME)

作者： Han, Wanjiang Jiang, Lixin Zou, Di Nie, Jiaqi Jin, Xin Gong, Feifei Chen, Shuwen Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China China Earthquake Networks Ctr Dept Informat Technol Support Beijing 100036 Peoples R China

Based on a large number of literatures and reports, this paper takes communication systems in lifeline engineering as an example, collects as many disaster-related communication data as possible to form a training data set, and analyzes the relationship between disaster-related telecommunications data and earthquake intensity. Then, use visual means to preprocess the collected data set. This paper uses different classification algorithms in machine learning, such as Naive Bayes, K-nearest neighbors, logistic regression and support vector machines for classification training of the formed data set. Optimize it, and finally it proposes a reference model that can be used to predict earthquake intensity. Finally, experiments show that the model has good accuracy.

关键词： Machine learning algorithm optimization Earthquake intensity prediction Telecommunications data Big data

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Power and Performance Characterization, Analysis and Tuning for Energy-efficient Edge Detection on Atom and ARM based Platforms 33

Power and Performance Characterization, Analysis and Tuning ...

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33rd IEEE International Conference on Computer Design (ICCD)

作者： Otto, Paul Malik, Maria Akhlaghi, Nima Sequeira, Rebel Homayoun, Houman Sikdar, Siddhartha George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (纸本)9781467371667

The de facto standard for embedded platforms with medium to low computing demands are ARM with Thumb ISA and Intel Atom with the X86 ISA with multiple cores. Operating these architectures in the milliwatts range while running realtime computer vision corner detection algorithms is a challenging problem. We present the analysis of power, performance and energy-efficiency measurements of Harris corner detection across a wide range of voltage and frequency settings, multicore/multithreading strategies, and compiler and application optimization parameters to find how the interplay of these parameters affect the power, performance and energy-efficiency. Our measurement of results on state-of-the-art embedded platforms demonstrate that a systematic cross-layer optimization at the application level (Sobel filter type, aperture size, number of image tiles), compiler level (branch prediction, function inlining) and system level (voltage and frequency setting, single core vs multicore implementation) significantly improves the energy-efficiency of corner detection, while meeting its real-time performance constraints. This cross-layer optimization improves the energy-efficiency of Harris corner on Atom and ARM by 89.5% and 87.2%, respectively.

关键词： Harris corner Detection Embedded Platforms Energy Delay Product algorithm optimization System optimization

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SwiftOGA: An Efficient and Swift Online Gradient Ascent algorithm for Caching Replacement 29

SwiftOGA: An Efficient and Swift Online Gradient Ascent Algo...

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29th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Dong, Bin Song, Tian Zhang, Qianyu Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350354249;9798350354232

Content caching plays a crucial role in improving the efficient retrieval of content and enabling fast delivery to enhance the quality of service (QoS). Traditional caching replacement policies (e.g., LRU, LFU) usually depend on historical request patterns to decide which content to store. However, they struggle to handle adversarial request patterns and dynamically changing popular content. Online learning caching policies (e.g., OGA) are resilient to different request patterns and can be applied in intricate network environments to address the caching replacement problem. Nevertheless, these policies tend to become more computationally intensive over time due to the increasing amount of content, leading to higher computing consumption. Motivated by this, we propose SwiftOGA, an efficient and swift online gradient ascent algorithm for cache replacement. Compared to previous online learning caching policies, our proposal achieves a reduction in computational overhead of at least 74.9%. Furthermore, it exhibits a cache hit ratio improvement of 8.3% over OGA under a dynamic request pattern. We also demonstrate that the proposed policy still has sub-linear regret.

关键词： Online Learning algorithm optimization Cache Replacement Policy

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Intelligent Prediction Platform of Lathe Machine Based on Back Propagation Neural Network

Intelligent Prediction Platform of Lathe Machine Based on Ba...

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IEEE International Conference on Advanced Manufacturing (IEEE ICAM)

作者： Chang, Wen-Yang Wu, Sheng-Jhih Lin, Bo-Shang Natl Formosa Univ Smart Machine & Intelligent Mfg Res Ctr Huwei Yunlin Taiwan Natl Formosa Univ Dept Mech & Comp Aided Engineer Huwei Yunlin Taiwan Natl Formosa Univ Dept Power Mech Engn Huwei Yunlin Taiwan Natl Formosa Univ Inst Mech & Comp Aided Engn Huwei Yunlin Taiwan

ISBN: (纸本)9781538656099

For the CNC machine tool, the processing parameters of cutting are a key factor to affect the manufacturing accuracy and tool wear. However, this study proposes a prediction system based on neural network algorithm to estimate the wear of turning tool. For neural network algorithm, the processing parameters, the cutting speed, feed rate and material removal rate are investigated as the input parameters of the BNN. The output parameters of the BNN are the wear of turning tool and the surface accuracy of workpiece. Experimental results showed that the turning cutting wear of prediction accuracy compared with the experiment is 93.44%. The max error of cutting wear between the prediction and the experiment is 15 mu m.

关键词： Backpropagation Neural Network algorithm optimization Turning Tool Wear Cutting prediction analysis

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Embedded Face Detection Implementation 12

Embedded Face Detection Implementation

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12th Annual International Conference of the Biometrics-Special-Interest-Group (BIOSIG) of the Gesellschaft-fur-Informatik (GI) e V

作者： Acasandrei, Laurentiu Barriga, Angel IMSE CNM CSIC Inst Microelect Sevilla Seville Spain

ISBN: (纸本)9783885796060

In this communication an embedded implementation of the Viola-Jones face detection algorithm targeting low frequency, low memory, and low power consumption, is presented. The design methodology, performance analysis and algorithm optimization in order to accelerate the face detection process, will be described. The resulted implementation is platform independent and achieves on average a 3 times detection speed up.

关键词： Facial Recognition algorithm optimization low power consumption platform-independent Detection algorithms embedding Power consumption

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