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A VLSI implementation of dual AC-3 and MPEG-2 audio decoder

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1998年第3期44卷 872-877页

作者： Ko, WS Yoo, SK Park, SW Kim, JS Youn, DH Yonsei Univ Dept Elect Engn ASSP Lab Seoul 120749 South Korea Yonsei Univ Dept Med Engn Seoul 120749 South Korea

This paper presents a dual AC-3 and MPEG-2 audio decoder which can decode both bit-streams. MPEG-2 synthesis filtering is modified by the 32-point FFT to share the common data path with the AC-3's. The architecture of the decoder consists of a programmable DSP cole and a hardwired common synthesis filter. The DSP core was designed suitable for audio processing. The common filter design adopts the suggested II MPEG-2 modified synthesis filtering. The proposed system was synthesized with 0.6 mu m technology and proven to be cost-effective.

关键词： Very large scale integration Decoding Filters Digital signal processing Algorithm design and analysis Filtering algorithms Hardware Logic design partitioning algorithms Read only memory

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Flow-Based Clustering on Directed Graphs: A Structural Entropy Minimization Approach

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IEEE ACCESS 2020年 8卷 152579-152591页

作者： Yao, Yuan Pan, Yicheng Wang, Shaojiang Wang, Hongan Nazeer, Waqas Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Beijing Key Lab Human Comp Interact Inst Software Beijing 100190 Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Govt Coll Univ Dept Math Lahore 54000 Pakistan

In this study, we focus on flow-based clustering on directed graphs and propose a localized algorithm for this problem. Flow-based clustering in networks requires a set of closely related vertices where the flow amount getting into it is larger than that going out of it. It is able to formulate a variety of practical problems, such as fund-raising set detection in financial networks and influential document clustering in citation networks, etc. Methodologically, we propose the new concept of two-dimensional structural entropy on directed graphs, and based on this, a local structural entropy minimization algorithm for detecting the flow-based community structure of networks is designed. We adopt our algorithm for the problem of fund-raising set detection in financial networks, in which vertices represent accounts, edges represent transactions between two accounts, and weights represent money amounts of transactions. In our experiments, the local two-dimensional structure entropy minimization algorithm is devoted to find a fund-raising community which involves a given input account. We conduct experiments on both synthetic and real fund-raising datasets. The experimental results demonstrate that, given a fixed account, our algorithm is able to efficiently locate a fund-raising community (if any) for which the fund flowing into the community is much higher than that flowing out, and the transactions within the community are relatively denser (fund amount based) than that of inter-community. For a synthetic ground-truth fund-raising community, we adjust the parameters to change its fund-raising tendency. The results for the synthetic datasets show that our algorithm obtains higher precision and recall rates as this tendency gets stronger with each single factor varing. For a real fund-raising community embedded in a simulated capital flow network, our algorithm also find it with high precision and recall rates. The experiments for both scenarios verify the effectiveness

关键词： Entropy Clustering algorithms Directed graphs Minimization partitioning algorithms Laboratories Software Network flows directed graph graph mining structural entropy

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A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

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IEEE ACCESS 2022年 10卷 98034-98047页

作者： Li, Fuxiang Zhou, Ming Li, Shu Yang, Tianhao Harbin Univ Sci & Technol Sch Sci Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Minist Educ Key Lab Engn Dielect & Applicat Harbin 150080 Peoples R China

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.

关键词： Clustering algorithms Resource management partitioning algorithms Classification algorithms Solid modeling Image segmentation Complexity theory Density measurement Clustering density peaks candidate cluster center cluster fusion strategy

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A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

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IEEE ACCESS 2022年 10卷 95219-95228页

作者： Li, Dejian Fang, Rongqiang Wang, Jing Zhao, Dongyan Chong, Ting Ren, Zengmin Ma, Jun Beijing Smartchip Microelect Technol Co Ltd Beijing 100192 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Renmin Univ China Sch Informat Beijing 100056 Peoples R China

Neural networks (NNs) have been widely adopted in various application domains. Deeper NNs greatly enhance the output accuracy, but complex NNs with more parameters incur intensive memory accesses, and the data usually need to be partitioned since it may exceed the on-chip storage. However, there is no research considering the partition and scheduling co-design of the NNs. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Further, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original, and the throughput of the AlexNet, VGG and VGG19 is increased to an average of 1.66 times.

关键词： Sparse matrices Neural networks partitioning algorithms Data models Optimization Scheduling Sparse matrix neural networks loop scheduling compression partition

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Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2022年 17卷 2015-2029页

作者： Xu, Qiang Jiang, Xinghao Sun, Tanfeng Kot, Alex C. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China City Univ Hong Kong Ctr Intelligent Multidimens Data Anal Hong Kong Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Natl Engn Lab Informat Content Anal Tech Shanghai 200240 Peoples R China Nanyang Technol Univ Singapore 637553 Singapore

High Efficiency Video Coding (HEVC) double compression detection is of prime significance in video forensics. However, double compression with the same parameters and video content with high motion displacement intensity have become two main factors that limit the performance of existing algorithms. To address these issues, a novel motion-adaptive algorithm is proposed in this paper. Firstly, the analysis of GOP structure in HEVC standard and the coding process of HEVC double compression are provided. Next, sub-features composed of fluctuation intensities of intra prediction modes and unstable Prediction Units (PUs) in normal Intra-Frames (I-frames) and optical flow in adaptive I-frames are exploited in our algorithm. Each sub-feature is extracted during the process of multiple decompression. We further combine these sub-features into a 27-dimensional detection feature, which is finally fed to the Support Vector Machine (SVM) classifier. By following a separation-fusion detection strategy, the experimental result shows that the proposed algorithm outperforms the existing state-of-the-art methods and demonstrates superior robustness to various motion displacement intensities and a wide variety of coding parameter settings.

关键词： Encoding Prediction algorithms Feature extraction Standards partitioning algorithms Detection algorithms Video sequences HEVC double compression detection motion displacement intensity intra prediction modes unstable prediction units optical flow

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AREA ROUTING FOR ANALOG LAYOUT

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1993年第8期12卷 1186-1197页

作者： MALAVASI, E SANGIOVANNIVINCENTELLI, A Dipartimento di Elettronica ed Informatica University of Padova Italy Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA USA

An area router specifically tailored for the layout of analog circuits is presented. It is based on the A* algorithm, which combines the flexibility of maze routing with computational efficiency. Parasitics are controlled by means of a programmable cost function based on a set of user-defined weights. The weights can be automatically defined based on high-level electrical performance specifications and determine the net scheduling. An algorithm for symmetric routing preserves symmetries in differential architectures. Different current paths can be dealt with in each wire by means of a net partitioning procedure driven by information on the current driven by terminals. Shields can be built between critically coupled wires, in order to guarantee an effective limitation of cross-coupling. The weight-driven programmable cost function makes this router particularly suitable for a performance-driven approach to analog routing. Automatic weight definition also makes the use of the tool independent of the user's expertise. The implemented algorithms are described and results are illustrated proving the effectiveness of this approach.

关键词： Routing Parasitic capacitance Analog circuits Cost function Wire Laboratories Automatic control Processor scheduling partitioning algorithms Computer architecture

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Analysis of Students' Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks

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IEEE ACCESS 2021年 9卷 132592-132608页

作者： Delgado, Soledad Moran, Federico San Jose, Jose Carlos Burgos, Daniel Univ Politecn Madrid Dept Sistemas Informat Madrid 28031 Spain Univ Complutense Madrid Dept Bioquim & Biol Mol Madrid 28040 Spain Univ Int La Rioja UNIR Res Inst Innovat & Technol Educ UNIR iTED Logrono 26006 Spain

An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts.

关键词： Self-organizing feature maps Neurons Clustering algorithms Training Shape Data mining partitioning algorithms Artificial neural networks data science applications in education distance education and online learning pattern analysis self-organizing map (SOM) student behaviour unsupervised learning

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NODIFS - SIMULATING FAULTS FAST

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IEEE CIRCUITS AND DEVICES MAGAZINE 1994年第5期10卷 26-38页

作者： GHOSH, S Division of Engineering Brown University

This article introduces, perhaps for the first time, an asynchronous, distributed, circuit partitioned algorithm that is capable of fault simulating both combinational and sequential digital designs on parallel processors. In this approach, called NODIFS (NOvel asynchronous DIstributed algorithm for Fault Simulation), every circuit component is modeled as an asynchronous and concurrent entity that is checked for faults as soon as appropriate signal transitions and fault lists are asserted at its input ports. The circuit is partitioned such that components of every partition are allocated to a unique processor of the parallel processor system.< >

关键词： Circuit faults Circuit simulation Circuit testing Algorithm design and analysis Discrete event simulation Process design partitioning algorithms Scalability System testing Instruments

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Optimal Modification of Peak-Valley Period Under Multiple Time-of-Use Schemes Based on Dynamic Load Point Method Considering Reliability

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第5期37卷 3889-3901页

作者： Yang, Hejun Gao, Yuan Ma, Yinghao Zhang, Dabo Hefei Univ Technol Anhui Prov Key Lab Renewable Energy Utilizat & En Hefei 230009 Peoples R China

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load-reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system.

关键词： Reliability Load modeling Power system reliability Clustering algorithms Optimization Heuristic algorithms partitioning algorithms Time-of-use load curve clustering period partition BP neural network reliability loss

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REDUCED-STATE SEQUENCE ESTIMATION FOR CODED MODULATION ON INTERSYMBOL INTERFERENCE CHANNELS

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 1989年第6期7卷 989-995页

作者： EYUBOGLU, MV QURESHI, SUH Codex Corporation Mansfield MA USA

The authors investigated the detection of trellis codes designed for channels that are intersymbol interference free when they operate in the presence of intersymbol interference. A well-structured reduced-state sequence estimation (RSSE) algorithm is described which can achieve the performance of maximum-likelihood sequence estimation (MLSE) with drastically reduced complexity. Well-defined reduced-state trellises are first constructed by merging the states of the ML supertrellis using set partitioning principles. Then the Viterbi algorithm is used to search these trellises. A special case of RSSE, called parallel decision-feedback decoding, uses the encoder trellis, yet on channels with large attenuation distortion it can provide a significantly better performance than linear equalization. The performance of RSSE is examined analytically and through simulation, and then compared to that of MLSE and ideal decision-feedback equalization. It is noted that the performance advantage of RSSE can be obtained without significantly increasing the decoding delay or complicating an adaptive implementation.< >

关键词： Modulation coding Maximum likelihood estimation Intersymbol interference Maximum likelihood decoding Decision feedback equalizers Convolutional codes partitioning algorithms Maximum likelihood detection Merging Viterbi algorithm

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