检索结果-内蒙古大学图书馆

Low-Energy Eigenspectrum Decomposition (LEED) of Quantum-Dot Cellular Automata Networks

IEEE TRANSACTIONS ON NANOTECHNOLOGY 2021年 20卷 104-112页

作者： Retallick, Jacob Walus, Konrad Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

The design and understanding of quantum-dot cellular automata (QCA) networks has been largely influenced by limitations in the approximation methods used in common design tools. In some cases, such limitations have led to unrealistic selections of clock zones which are not feasible for nanoscale QCA implementations given current fabrication constraints on clocking electrodes. A better understanding of the behaviour of larger QCA networks of perhaps tens to hundreds of QCA devices is needed. One approach is by investigating the low energy spectrum;however, diagonalization of the system Hamiltonian even in the 2-state approximation is impractical beyond 20 or so devices. In this work, we present a methodology for understanding the spectrum of the full network in terms of contributions from components of the network. We show that important features of the low energy spectrum can be attributed to specific critical components, and present one scheme for decomposing the network into these components. In addition, we address the question of computing the low energy spectrum of large QCA networks. A method based on basis reduction which naturally emerges from the component decomposition is successfully applied to a 49 cell XOR gate with results compared against a density matrix renormalization group implementation.

关键词： Clocks Stationary state Correlation Computer architecture Quantum dots Microprocessors Nanoscale devices Quantum dots nanotechnology approximation methods partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization algorithms

引用

IEEE ACCESS 2021年 9卷 93703-93722页

作者： Merikhi, Behnaz Soleymani, M. R. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic nu

关键词： Clustering algorithms Optimization partitioning algorithms Distortion Approximation algorithms Particle swarm optimization Heuristic algorithms Automatic data clustering binary search space binary clustering cluster-level constraint distortion dynamic clusters nature-inspired optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Fast Screen Content Coding in HEVC Using Machine Learning

引用

IEEE ACCESS 2021年 9卷 154659-154666页

作者： Badry, Emad Inoue, Koji Sayed, Mohammed Sharaf Kyushu Univ E JUST Ctr Fukuoka 8190382 Japan Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria 21934 Egypt Suez Canal Univ Dept Elect Engn Ismailia 41522 Egypt Kyushu Univ Dept Adv Informat Technol Fukuoka 8190382 Japan Kyushu Univ Syst LSI Res Ctr Fukuoka 8190382 Japan Zagazig Univ Dept Elect & Commun Engn Zagazig 44519 Egypt

Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new tools such as Intra Block Copy (IBC) and Palette (PLT) mode. These tools improve the coding efficiency, but they come with a huge computation complexity. In this paper, we propose a scheme to reduce the encoding time of SC encoder. It has two algorithms based on the Decision Rule (DR) machine learning technique. The first algorithm, which is called Mode Skipping (MS), is used to skip the unnecessary SC mode checking. Early Pruning Termination (EPT), which is the second algorithm, is used to stop the partitioning process early. A small number of features has to be calculated for the trained models. The proposed scheme was implemented then simulated by using the standard software test model SCM-6. The experimental results show that the time complexity is reduced by 37.84% on average while the Bjontegaard delta bit-rate (BD-R) increases by 1.34% only.

关键词： Copper Encoding Tools Costs partitioning algorithms Testing Training High efficiency video coding (HEVC) screen content (SC) decision rule

来源：评论

学校读者我要写书评

暂无评论

Asynchronous Distributed Event-Triggered Coordination for Multiagent Coverage Control

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 5941-5953页

作者： Ajina, Mohanad Tabatabai, Daniel Nowzari, Cameron George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

This article revisits a multiagent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this article, we propose a fully asynchronous communication aware solution to the multiagent deployment problem that uses an event-triggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence, and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.

关键词： Sensors Broadcasting partitioning algorithms Wireless sensor networks Task analysis Monitoring Uncertainty Event-triggered control optimal control outdated information robotic networks spatial partitioning Voronoi diagram

来源：评论

学校读者我要写书评

暂无评论

KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors

引用

IEEE ACCESS 2021年 9卷 152616-152627页

作者： Kim, Jeong-Hun Choi, Jong-Hyeok Park, Young-Ho Leung, Carson Kai-Sang Nasridinov, Aziz Chungbuk Natl Univ Dept Comp Sci Cheongju 28644 South Korea Chungbuk Natl Univ Bigdata Res Inst Cheongju 28644 South Korea Sookmyung Womens Univ Dept IT Engn Seoul 04310 South Korea Univ Manitoba Dept Comp Sci Winnipeg MB R3E 0W2 Canada

Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to incorrect clustering results because of high sensitivity to noise points. In this study, we propose a robust spectral clustering algorithm known as KNN-SC that can discover exact clusters by decreasing the influence of noise points. To achieve this goal, we present a novel approach that filters out potential noise points by estimating the density difference between data points using k-nearest neighbors. In addition, we introduce a novel method for generating a similarity graph in which various densities of data points are effectively represented by expanding the nearest neighbor graph. Experimental results on synthetic and real-world datasets demonstrate that KNN-SC achieves significant performance improvement over many state-of-the-art spectral clustering algorithms.

关键词： Clustering algorithms partitioning algorithms Symmetric matrices Laplace equations Optimization Minimization Licenses k-nearest neighbors nearest neighbor graph potential noise detection spectral clustering

来源：评论

学校读者我要写书评

暂无评论

An Improved Numerical DBSCAN Algorithm Based on Non-IIDness Learning

引用

IEEE ACCESS 2021年 9卷 117052-117066页

作者： Wang, Yanmei Jiang, He Zhou, Huixin Wang, Chengqiang Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China

In clustering algorithm research, objects, attributes and other aspects of data sets are usually considered to be independent and identically distributed;that is, each object is assumed to be an independent and uniformly distributed individual with no impacts between objects. However, objects in real life are often neither independently nor identically distributed;that is, they are non-IID, leading to a complex coupling relationship between objects, and objects interact with each other. The results of a clustering algorithm under an independent and identical distribution may be incomplete or even misleading. To make the results of the DBSCAN algorithm as accurate as possible, an improved numerical DBSCAN algorithm based on non-IIDness learning is proposed in this paper. The algorithm calculates the coupling relationship between objects to obtain the potential relationship between objects and determines the parameters Eps and MinPts by the distribution characteristics of the data. Experiments on large-scale real and synthetic data sets show that the algorithm achieves a higher accuracy than the original DBSCAN algorithm and the main algorithms that improved upon it.

关键词： Clustering algorithms Couplings Licenses partitioning algorithms Correlation Time series analysis Sorting Clustering DBSCAN parameter selection non-IIDness learning

来源：评论

学校读者我要写书评

暂无评论

Iterated Watersheds, A Connected Variation of K-Means for Clustering GIS Data

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第2期9卷 626-636页

作者： Soor, Sampriti Challa, Aditya Danda, Sravan Sagar, B. S. Daya Najman, Laurent Indian Stat Inst Syst Sci & Informat Unit Bangalore 560059 Karnataka India Univ Paris Est ESIEE Paris ENPC LIGM UMR 8049CNRSUPEMLV F-93162 Noisy Le Grand France

In this article, we propose a novel algorithm to obtain a solution to the clustering problem with an additional constraint of connectivity. This is achieved by suitably modifying K-Means algorithm to include connectivity constraints. The modified algorithm involves repeated application of watershed transform, and hence is referred to as iterated watersheds. Detailed analysis of the algorithm is performed using toy examples. Iterated watersheds is compared with several image segmentation algorithms. It has been shown that iterated watersheds performs better than methods such as spectral clustering, isoperimetric partitioning, and K-Means on various measures. To illustrate the applicability of iterated watersheds - a simple problem of placing emergency stations and suitable cost function is considered. Using real world road networks of various cities, iterated watersheds is compared with K-Means and greedy K-center methods. It is observed that iterated watersheds result in 4 - 66 percent improvement over K-Means and in 31 - 72 percent improvement over Greedy K-Centers in experiments on road networks of various cities.

关键词： Clustering algorithms partitioning algorithms Cost function Approximation algorithms Image segmentation Roads Graph clustering K-means E-governance watersheds

来源：评论

学校读者我要写书评

暂无评论

Person Re-Identification Based on Partition Adaptive Network Structure and Channel Partition Weight Adaptive

引用

IEEE ACCESS 2021年 9卷 101402-101413页

作者： Chen, Wenjie Yang, Fan Chongqing Coll Elect Engn Chongqing 401331 Peoples R China ZTE Corp Nanjing Res & Dev Ctr Nanjing 210012 Peoples R China

This article noticed that the feature of the block in human body image is noticeable, some algorithms in pedestrian re-identification are based on partitioning the human body to calculate the similarity between pedestrians images. However, it is not easy to find a proper method to partition the pedestrian image. This article proposes a method partition adaptive network structure (PANS) to automatically determine the partition scheme. This method can save much work to find a suitable partitioning scheme, and the effect of the automatic partitioning scheme is better than that of the manual partitioning scheme. In addition, this paper proposes a pedestrian re-recognition method based on channel partition weight adaptive (CPWA). We obtained better results on three public pedestrian re-identification data sets compared with the baseline. This method, combined with the automatic partitioning scheme proposed in this article, can improve results. We have done experiments on the three public data sets of market-1501, DukeMTMC-Reid, and CUHK03, proving the superiority of the two methods proposed in this article.

关键词： Feature extraction Adaptive systems partitioning algorithms Manuals Deep learning Adaptation models Training Channel-partition-weight-adaptive deep learning partition-adaptive-network-structure person re-identification

来源：评论

学校读者我要写书评

暂无评论

Event Detection for Energy Consumption Monitoring

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

引用

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2021年第4期6卷 703-709页

作者： Jorde, Daniel Jacobsen, Hans-Arno Tech Univ Munich Dept Comp Sci D-80333 Munich Germany

The accurate detection of appliance state transitions in electrical signals is fundamental for numerous energy-conserving applications. We present an extensive overview and categorization of the current state in event detection on high-sampling-rate signals. Existing approaches are designed for specific environments and need to be tediously adapted for new ones. Thus, we propose an unsupervised, multi-environment event detector, outperforming four state-of-the-art algorithms on two heterogeneous public datasets.

关键词： Event detection Measurement Machine learning Detectors Energy consumption Approximation algorithms partitioning algorithms Event detection non-intrusive load monitoring neural nets machine learning energy-aware systems

来源：评论

学校读者我要写书评

暂无评论

Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4604-4619页

作者： Cannelli, Loris Facchinei, Francisco Scutari, Gesualdo Kungurtsev, Vyacheslav Ist Dalle Molle Intelligenza Artificiale IDSIA USI SUPSI CH-6900 Lugano Switzerland Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Univ Roma La Sapienza Dept Comp Control Arid Management Engn I-00185 Rome Italy Czech Tech Univ Dept Comp Sci Prague 16636 Czech Republic

We consider convex and nonconvex constrained optimization with a partially separable objective function: Agents minimize the sum of local objective functions, each of which is known only by the associated agent and depends on the variables of that agent and those of a few others. This partitioned setting arises in several applications of practical interest. We propose what is, to the best of our knowledge, the first distributed, asynchronous algorithm with rate guarantees for this class of problems. When the objective function is nonconvex, the algorithm provably converges to a stationary solution at a sublinear rate whereas linear rate is achieved under the renowned Luo-Tseng error bound condition (which is less stringent than strong convexity). Numerical results on matrix completion and LASSO problems show the effectiveness of our method.

关键词： Delays Optimization Convergence partitioning algorithms Nickel Linear programming Indexes Asynchronous algorithms error bounds linear rate multiagent systems nonconvex optimization

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：