Monte Carlo based statistical timing analysis using an efficient sampling method
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Statistical static timing analysis (SSTA) is one of the most commonly used methods for analyzing timing behavior under process variations (PVs). Monte Carlo (MC) simulation is a straightforward and effective method for SSTA. In this paper, a new sampling method is proposed to improve an MC-based approach by reducing the number of simulation samples. This method utilizes stratification with an importance analysis for the selection of samples of PVs. Extensive simulations are performed to show that the proposed method achieves a better precision on ISCAS 89 benchmarks compared to other MC simulation approaches.Buffer (optical fiber)
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Model Selection is a task selecting set of potential models.This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables.In this paper, the selection of the correct number of latent variables is critical.In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes.This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA.During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward.To treat this problem, we introduce a new combined pruning method which selects the best options for removal, has been used.The obtained results show that this algorithm leads to an optimized number of latent variables.In this paper, we propose a novel approach, namely DPMFS, to address this issue.
Document Clustering
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At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them.When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria.This is because an inefficient center may result in unpredicted outcomes.Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers.We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure.They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices.The clustering accuracy was also conducted using Rand index criteria.All the algorithms computational complexity was analyzed and finally their computation time was also recorded.As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers.More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.
Center (category theory)
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To improve the effectiveness and QoS of service in a sensor network there are number of communication and localization architectures followed by sensor network.One of such architecture is Leader Selection Architecture.This architecture restrict the communication to short distances so that the energy consumption of a node is reduces.In this paper, an improved approach is defined to perform the selection of Leader.This leader selection architecture approach is defined under multiple parameters including the energy, connectivity analysis and the balancing over the network.The improvement is also performed to generate the safe communication over the network controlled by the leader node.The obtained results show that the work has improved the network communication as well as network life.
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This paper presents a Multi-objective Quantum-inspired Hybrid Differential Evolution (MQHDE) for the solution of software requirements selection problem and its application on a real-world project.As the customer requirements change from time to time, often software products are developed in an iterative or incremental manner so as to deal with these changing requirements.The problem is to identify a set of requirements to be included in the next release of the software product, by minimizing the cost and maximizing the customer satisfaction.This problem is referred to as Multi-objective Next Release Problem (MONRP) in the jargon of Searchbased Software Engineering (SBSE).The solution to the problem of MONRP has been studied by researchers using different metaheuristic search techniques.The efficiency of the proposed MQHDE is tested on a real-world application and the results are compared against the state-of-the-art multiobjective evolutionary algorithm NSGA-II, and found that the performance of MQHDE is promising and therefore can be used with confidence for the solution of real-world instances of MONRP.
Search-based software engineering
Differential Evolution
Software requirements
Jargon
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Destage algorithms play an important role in improving IO performance in cached RAID5 systems.In this paper,the authors study a number of existing destage algorithms,and propose a destage-efficiency-first(DEF) algorithm based on the classification of system status for RAID5 systems.The main idea of this algorithm is that destage strategy will be dynamically adjusted on the principle of detage efficiency according to the current cache occupancy level and varying workload.In addition,the authors present the key technology in implementing the DEF algorithm,and test and analyse the performance of the algorithm.
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Zipf's law
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This paper presents mainly the algorithms used in a video format conversion SoC, including weighted sum motion-adaptive de-interlacing algorithms, multi-level pipelined filtering algorithm used in scaling and DDA algorithm. These algorithms take hardware implementation ability and resource utilization rate into consideration, thus satisfy requirements of the format conversion SoC. In the end, system level simulation result is given.
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