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Behrooz Parhami
School of Information, Yunnan University of Finance and Economics, Kunming 650221 China

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Journal article
Published: 17 March 2021 in IEEE Access
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Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic community. However, spectral clustering still cannot well handle some shapes of sample distributions in the space of extracted features, and density peak clustering encounters performance problems because it cannot mine the local structures of data and well deal with non-uniform distributions. In order to solve above problems, we propose the density gain-rate peak clustering (DGPC), a new type of density peak clustering method, and then embed it in spectral clustering for performance promotion. Firstly, in order to well handle non-uniform sample distributions, we propose density gain-rate for density peak clustering. Density gain-rate is based on the assumption that the density of a clustering center will be higher with the reduce of the radius. Even under non-uniform distributions, the cluster center in low density region will still have a significant density gain-rate thus can be detected. We combine density gain-rate in density peak clustering to construct DGPC method. Then in the framework of spectral clustering, we use our new density peak clustering to cluster the samples by their extracted features from a similarity graph of these samples, such as the neighbor-based similarity graph or the self-expressiveness similarity graph. Compared with the previous spectral clustering and density peak clustering, our method leads to better clustering performances on varying shapes of sample distributions. The experiment measures the performances of our clustering method and existing clustering methods by NMI and ACC on seven real-world datasets to illustrate the effectiveness of our method.

ACS Style

Jiexing Liu; Chenggui Zhao. Density Gain-Rate Peaks for Spectral Clustering. IEEE Access 2021, PP, 1 -1.

AMA Style

Jiexing Liu, Chenggui Zhao. Density Gain-Rate Peaks for Spectral Clustering. IEEE Access. 2021; PP (99):1-1.

Chicago/Turabian Style

Jiexing Liu; Chenggui Zhao. 2021. "Density Gain-Rate Peaks for Spectral Clustering." IEEE Access PP, no. 99: 1-1.

Journal article
Published: 29 August 2020 in Computers & Electrical Engineering
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The logarithmic number system (LNS) has found appeal in digital arithmetic because it allows multiplication and division to be performed much faster and more accurately than with widely-used floating-point (FP) number formats. We present a comprehensive review and comparison of various techniques and architectures for performing arithmetic operations efficiently in LNS, using the sign/logarithm format, and focus on the European Logarithmic Microprocessor (ELM), a device built in the framework of a research project launched in 1999, as an important case study. Comparison of the arithmetic performance of ELM with that of a commercial superscalar pipelined FP processor of the same vintage and technology confirms that LNS has the potential for successful deployment in general-purpose systems. Besides paying due attention to LNS attributes beyond computational speed and accuracy, novel contributions of this survey include an exploration of the relationship of LNS with the emerging field of approximate computing and a discussion of the discrete logarithmic number system.

ACS Style

Behrooz Parhami. Computing with logarithmic number system arithmetic: Implementation methods and performance benefits. Computers & Electrical Engineering 2020, 87, 106800 .

AMA Style

Behrooz Parhami. Computing with logarithmic number system arithmetic: Implementation methods and performance benefits. Computers & Electrical Engineering. 2020; 87 ():106800.

Chicago/Turabian Style

Behrooz Parhami. 2020. "Computing with logarithmic number system arithmetic: Implementation methods and performance benefits." Computers & Electrical Engineering 87, no. : 106800.

Journal article
Published: 06 February 2020 in Computers & Electrical Engineering
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Today's computing is increasingly data-intensive, heralding the age of big data. With greater data volumes, come the needs for faster processing, greater storage capacity, and expanded communication bandwidth, all of which imply the expenditure of more energy. Thus, energy efficiency, already a major design consideration, will assume broader significance in the coming years. As important as storage and communications are, our focus in this paper is on better technology to reduce the computation (logic manipulation) power. We review majority logic, a special case of threshold logic, show how a number of common arithmetic/logic operations can be performed using the majority-gate primitive, and review an impressive array of atomic-scale logic technologies that are particularly efficient in realizing the majority or minority function. We conclude that a combination of orders of magnitude energy reduction by virtue of the technology used and implementation strategies that lead to comparable complexity in terms of majority gates when contrasted with currently used circuit primitives (AND, OR, XOR, NOT, mux) leads to energy-efficient realization of arithmetic/logic functions suitable for use in the age of big data.

ACS Style

Behrooz Parhami; Dariush Abedi; Ghassem Jaberipur. Majority-Logic, its applications, and atomic-scale embodiments. Computers & Electrical Engineering 2020, 83, 106562 .

AMA Style

Behrooz Parhami, Dariush Abedi, Ghassem Jaberipur. Majority-Logic, its applications, and atomic-scale embodiments. Computers & Electrical Engineering. 2020; 83 ():106562.

Chicago/Turabian Style

Behrooz Parhami; Dariush Abedi; Ghassem Jaberipur. 2020. "Majority-Logic, its applications, and atomic-scale embodiments." Computers & Electrical Engineering 83, no. : 106562.

Conference paper
Published: 26 January 2020 in Communications in Computer and Information Science
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Network virtualization is an effective approach to solve the problem of high cost of reconstructing underlying hardware facilities. Network virtualization enables a substrate network to share multiple virtual networks. In order to achieve higher performance with a lower cost, how to effectively embed virtual network to substrate network has become a key issue. Regarding this, we design and implement a heuristic virtual network embedding algorithm based on core and coritivity of graph. This algorithm finds the core nodes in the virtual network and embeds them to the substrate network. The performance of the proposed algorithm is evaluated in comparison with three other algorithms in experiments. Consequently, proposed algorithm is more efficient and improves apparently the runtime of virtual network requests.

ACS Style

Jie Yang; Chenggui Zhao. Virtual Network Embedding Based on Core and Coritivity of Graph. Communications in Computer and Information Science 2020, 71 -82.

AMA Style

Jie Yang, Chenggui Zhao. Virtual Network Embedding Based on Core and Coritivity of Graph. Communications in Computer and Information Science. 2020; ():71-82.

Chicago/Turabian Style

Jie Yang; Chenggui Zhao. 2020. "Virtual Network Embedding Based on Core and Coritivity of Graph." Communications in Computer and Information Science , no. : 71-82.

Research article
Published: 03 September 2019 in Transactions on Emerging Telecommunications Technologies
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To alleviate the computational burden of previous virtual network embedding (VNE) approaches when the resource network scales up significantly, we propose an efficient node ranking strategy that considers both global and local topological characteristics of the substrate network in mapping virtual nodes to physical nodes. This method ranks the substrate network nodes in two stages. First, all nodes are ranked globally with respect to the stationary distribution of the entire network. Then, a connected subset of the ranked substrate nodes, forming the H‐admissible embedding subgraph, is extracted. Finally, the subgraph nodes are ranked according to a local node ranking vector derived from a random‐walking scheme. The local rank vector is resolved using discrete Green's function satisfying the Dirichelet boundary condition. The more accurate association of node demands and resources that our proposed method provides leads to both better acceptance ratio and lower computational overhead. These claims have been justified via theoretical and algorithmic presentation of our scheme and offer experimental results obtained through simulation, to confirm its execution efficiency and solution quality compared with a couple of previous VNE proposals.

ACS Style

Chenggui Zhao; Behrooz Parhami. Virtual network embedding on massive substrate networks. Transactions on Emerging Telecommunications Technologies 2019, 31, 1 .

AMA Style

Chenggui Zhao, Behrooz Parhami. Virtual network embedding on massive substrate networks. Transactions on Emerging Telecommunications Technologies. 2019; 31 (2):1.

Chicago/Turabian Style

Chenggui Zhao; Behrooz Parhami. 2019. "Virtual network embedding on massive substrate networks." Transactions on Emerging Telecommunications Technologies 31, no. 2: 1.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Parallel Processing with Big Data. Encyclopedia of Big Data Technologies 2019, 1253 -1259.

AMA Style

Behrooz Parhami. Parallel Processing with Big Data. Encyclopedia of Big Data Technologies. 2019; ():1253-1259.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Parallel Processing with Big Data." Encyclopedia of Big Data Technologies , no. : 1253-1259.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Energy Implications of Big Data. Encyclopedia of Big Data Technologies 2019, 722 -728.

AMA Style

Behrooz Parhami. Energy Implications of Big Data. Encyclopedia of Big Data Technologies. 2019; ():722-728.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Energy Implications of Big Data." Encyclopedia of Big Data Technologies , no. : 722-728.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Tabular Computation. Encyclopedia of Big Data Technologies 2019, 1667 -1672.

AMA Style

Behrooz Parhami. Tabular Computation. Encyclopedia of Big Data Technologies. 2019; ():1667-1672.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Tabular Computation." Encyclopedia of Big Data Technologies , no. : 1667-1672.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Data Longevity and Compatibility. Encyclopedia of Big Data Technologies 2019, 559 -563.

AMA Style

Behrooz Parhami. Data Longevity and Compatibility. Encyclopedia of Big Data Technologies. 2019; ():559-563.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Data Longevity and Compatibility." Encyclopedia of Big Data Technologies , no. : 559-563.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Computer Architecture for Big Data. Encyclopedia of Big Data Technologies 2019, 481 -487.

AMA Style

Behrooz Parhami. Computer Architecture for Big Data. Encyclopedia of Big Data Technologies. 2019; ():481-487.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Computer Architecture for Big Data." Encyclopedia of Big Data Technologies , no. : 481-487.

Book chapter
Published: 20 February 2019 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Data Replication and Encoding. Encyclopedia of Big Data Technologies 2019, 579 -583.

AMA Style

Behrooz Parhami. Data Replication and Encoding. Encyclopedia of Big Data Technologies. 2019; ():579-583.

Chicago/Turabian Style

Behrooz Parhami. 2019. "Data Replication and Encoding." Encyclopedia of Big Data Technologies , no. : 579-583.

Journal article
Published: 25 January 2019 in IEEE Transactions on Network and Service Management
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ACS Style

Chenggui Zhao; Behrooz Parhami. Virtual Network Embedding Through Graph Eigenspace Alignment. IEEE Transactions on Network and Service Management 2019, 16, 632 -646.

AMA Style

Chenggui Zhao, Behrooz Parhami. Virtual Network Embedding Through Graph Eigenspace Alignment. IEEE Transactions on Network and Service Management. 2019; 16 (2):632-646.

Chicago/Turabian Style

Chenggui Zhao; Behrooz Parhami. 2019. "Virtual Network Embedding Through Graph Eigenspace Alignment." IEEE Transactions on Network and Service Management 16, no. 2: 632-646.

Reference work
Published: 01 May 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Data Longevity and Compatibility. Encyclopedia of Big Data Technologies 2018, 1 -5.

AMA Style

Behrooz Parhami. Data Longevity and Compatibility. Encyclopedia of Big Data Technologies. 2018; ():1-5.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Data Longevity and Compatibility." Encyclopedia of Big Data Technologies , no. : 1-5.

Reference work
Published: 27 April 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Parallel Processing with Big Data. Encyclopedia of Big Data Technologies 2018, 1 -7.

AMA Style

Behrooz Parhami. Parallel Processing with Big Data. Encyclopedia of Big Data Technologies. 2018; ():1-7.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Parallel Processing with Big Data." Encyclopedia of Big Data Technologies , no. : 1-7.

Reference work
Published: 26 April 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Computer Architecture for Big Data. Encyclopedia of Big Data Technologies 2018, 1 -7.

AMA Style

Behrooz Parhami. Computer Architecture for Big Data. Encyclopedia of Big Data Technologies. 2018; ():1-7.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Computer Architecture for Big Data." Encyclopedia of Big Data Technologies , no. : 1-7.

Journal article
Published: 25 April 2018 in Entropy
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For embedding virtual networks into a large scale substrate network, a massive amount of time is needed to search the resource space even if the scale of the virtual network is small. The complexity of searching the candidate resource will be reduced if candidates in substrate network can be located in a group of particularly matched areas, in which the resource distribution and communication structure of the substrate network exhibit a maximal similarity with the objective virtual network. This work proposes to discover the optimally suitable resource in a substrate network corresponding to the objective virtual network through comparison of their graph entropies. Aiming for this, the substrate network is divided into substructures referring to the importance of nodes in it, and the entropies of these substructures are calculated. The virtual network will be embedded preferentially into the substructure with the closest entropy if the substrate resource satisfies the demand of the virtual network. The experimental results validate that the efficiency of virtual network embedding can be improved through our proposal. Simultaneously, the quality of embedding has been guaranteed without significant degradation.

ACS Style

Jingjing Zhang; Chenggui Zhao; Honggang Wu; Minghui Lin; Ren Duan. Virtual Network Embedding Based on Graph Entropy. Entropy 2018, 20, 315 .

AMA Style

Jingjing Zhang, Chenggui Zhao, Honggang Wu, Minghui Lin, Ren Duan. Virtual Network Embedding Based on Graph Entropy. Entropy. 2018; 20 (5):315.

Chicago/Turabian Style

Jingjing Zhang; Chenggui Zhao; Honggang Wu; Minghui Lin; Ren Duan. 2018. "Virtual Network Embedding Based on Graph Entropy." Entropy 20, no. 5: 315.

Reference work
Published: 24 April 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Energy Implications of Big Data. Encyclopedia of Big Data Technologies 2018, 1 -7.

AMA Style

Behrooz Parhami. Energy Implications of Big Data. Encyclopedia of Big Data Technologies. 2018; ():1-7.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Energy Implications of Big Data." Encyclopedia of Big Data Technologies , no. : 1-7.

Reference work
Published: 23 April 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Tabular Computation. Encyclopedia of Big Data Technologies 2018, 1 -6.

AMA Style

Behrooz Parhami. Tabular Computation. Encyclopedia of Big Data Technologies. 2018; ():1-6.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Tabular Computation." Encyclopedia of Big Data Technologies , no. : 1-6.

Reference work
Published: 20 April 2018 in Encyclopedia of Big Data Technologies
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ACS Style

Behrooz Parhami. Data Replication and Encoding. Encyclopedia of Big Data Technologies 2018, 1 -5.

AMA Style

Behrooz Parhami. Data Replication and Encoding. Encyclopedia of Big Data Technologies. 2018; ():1-5.

Chicago/Turabian Style

Behrooz Parhami. 2018. "Data Replication and Encoding." Encyclopedia of Big Data Technologies , no. : 1-5.

Journal article
Published: 11 March 2018 in Symmetry
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Virtual network embedding (VNE) is a key technology in network virtualization. Advantages of network symmetry are well known in the design of load-balanced routing algorithms and in network performance analysis. Our work in this paper shows that benefits of graph symmetry also extend to the domain of network embedding. Specifically, we propose an efficient VNE method based on modular and structured agency guidance, a regular graph function. The proposed method, which is based on symmetric intermediate graphs, offers two main advantages. Firstly, characteristics of the intermediate structures enhance the computational efficiency of the VNE process. Secondly, the static agency network modeled with such intermediate structures improves the quality of the resulting embedding. These two advantages of our method are elaborated upon and verified by examples and simulations, respectively. In addition, we present a theoretical analysis explaining the reasons behind the benefits offered by such middleware.

ACS Style

Chenggui Zhao; Behrooz Parhami. Symmetric Agency Graphs Facilitate and Improve the Quality of Virtual Network Embedding. Symmetry 2018, 10, 63 .

AMA Style

Chenggui Zhao, Behrooz Parhami. Symmetric Agency Graphs Facilitate and Improve the Quality of Virtual Network Embedding. Symmetry. 2018; 10 (3):63.

Chicago/Turabian Style

Chenggui Zhao; Behrooz Parhami. 2018. "Symmetric Agency Graphs Facilitate and Improve the Quality of Virtual Network Embedding." Symmetry 10, no. 3: 63.