This paper presents a parallel motion estimation algorithm on Graphics Processing Units (GPU) with a GPU-based fast Coding Unit (CU) splitting mechanism for speeding up the execution speed of High Efficiency Video Cod...
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ISBN:
(纸本)9781509035267
This paper presents a parallel motion estimation algorithm on Graphics Processing Units (GPU) with a GPU-based fast Coding Unit (CU) splitting mechanism for speeding up the execution speed of High Efficiency Video Coding (HEVC). Parallel motion estimation algorithms only offer motion vectors to HEVC encoder, but CU splitting decision in HEVC still needs more information to speed up the encoder. Therefore, a mechanism using GPU to signify encoder which CU depth can be split instantly using motion data is designed. With experiments on Kepler GK110 GPU, the proposed parallel algorithm gains over 510 times faster than the full search motion estimation in the HEVC reference software, and the proposed fast CU splitting mechanism can further save 23% total execution time when compared to the general encoder with parallel motion estimation.
Machine Condition Monitoring (MCM) is an important topic for the reliability of industrial machines in increasingly interconnected production facilities. The analysis of a huge amount of data to get information about ...
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ISBN:
(纸本)9781509028719
Machine Condition Monitoring (MCM) is an important topic for the reliability of industrial machines in increasingly interconnected production facilities. The analysis of a huge amount of data to get information about the machine's condition is a difficult challenge. Current solutions for these analyses are often very specific, need a lot of manual configuration or are difficult to apply. In this paper, we present a system that uses anomaly detection in data streams to find hints for faulty machines in the data. The basis of this system is a Data stream management system (DSMS), which can handle huge amounts of streaming data and simplifies the definition of analyses. Due to the anomaly detection algorithms, the approach can be applied to a variety of data and scenarios. The outcome is a system that allows live analysis of machine data for MCM.
In this paper, we extended our recent modified approximate proximal point algorithm (APPA) published in [16] to solve system of monotone variational inequalities (SMVI). Under some mild conditions, we show that the pr...
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ISBN:
(纸本)9781509007523
In this paper, we extended our recent modified approximate proximal point algorithm (APPA) published in [16] to solve system of monotone variational inequalities (SMVI). Under some mild conditions, we show that the proposed method is globally convergent. The results presented in this paper generalize and improve the inexact proximal point algorithm for SMVI proposed in [17]. The proposed method is quite general and flexible and includes several well-known results.
This research work addresses a comparative examination of the two basic non-isolated DC-DC converters that could be interfaced effectively for maximum power point tracking (MPPT) in photovoltaic (PV) systems via track...
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ISBN:
(纸本)9781509002115
This research work addresses a comparative examination of the two basic non-isolated DC-DC converters that could be interfaced effectively for maximum power point tracking (MPPT) in photovoltaic (PV) systems via tracking algorithm of controlling the duty ratio of these converters. Examination of two famous DC-DC convertor topologies i.e. buck, and buck-boost converters has been performed here to scrutinize the behavior of converter behavior relating to changing atmospheric attributes, sequentially the deviation in the duty ratio (due to MPPT), and tracking efficiency. With the variant in the atmospheric conditions, the working value of resistance at the maximum power point (Rmpp) varies. In order to efficiently operate the system at the maximum power point, the MPPT algorithm must make the system work near to the value of Rmpp for the intermittent atmospheric pattern of varying insolation and temperature. The effectiveness of the MPPT algorithm can be scaled by this very obligation. The simulation study verifies that, although buck, and buck-boost converters are implemented as power converters for MPPT control, they are don't equally efficient. The consequence of diverse loads having values different to Rmpp on converter-side output is analyzed for the two important topologies, and it is inferred that the buck-boost converter topology most efficiently tracks the maximum power point (MPP) in case of varying temperature, insolation, and loading effect.
algorithmic parallelism arises naturally for population-based evolutionary algorithms. In this paper, a subpopulation-based parallel Cuckoo Search (CS) algorithm on OpenMP (Open Multi-Processing) for Traveling Salesma...
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ISBN:
(纸本)9781509025503
algorithmic parallelism arises naturally for population-based evolutionary algorithms. In this paper, a subpopulation-based parallel Cuckoo Search (CS) algorithm on OpenMP (Open Multi-Processing) for Traveling Salesman Problem (TSP) is proposed. The obligate brood parasitism behavior and mapping of the CS to TSP are explored to design the parallelization approach on OpenMP's fork-join model. The proposed parallel algorithm has been tested with symmetric instances from TSPLIB. Results show the subpopulation-based CS via random walk achieved superlinear speedup up to 42× and 1054% efficiency on OpenMP running 4 cores processor with superior percentage deviation against TSPLIB optimal solutions on small cities ranging from 51 to 101 cities, and only started to deviate significantly with 4461 cities. OpenMP subpopulation-based CS speedup also recorded at least 17× and up to 36× higher than related works in parallel CS. Overall results demonstrate that multi-threaded parallelism is very effective to achieve speedup for population-based evolutionary algorithms by dividing the main population into subpopulations to increase diversity on solution exploration.
Because the English and Castilian have marked acoustic and phonetic differences, this paper shows the study of the effectiveness of different algorithms VAD (Voice Activity Detection) literature, applied to the Castil...
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Because the English and Castilian have marked acoustic and phonetic differences, this paper shows the study of the effectiveness of different algorithms VAD (Voice Activity Detection) literature, applied to the Castilian, especially riplatense. This article is intended to publicize the results achieved to date. In the first part of the document briefly explained the three implemented methods, namely the autocorrelation function short time (STACF), the average magnitude of the differential junction (FDMA) and the linear prediction coefficients (LPC). Immediately, tests and experiments with BEPPA battery to evaluate the effectiveness of these algorithms VAD will be described. In this step 10 sentences were applied in selected Rioplatense Spanish BEPPA battery of each VAD to detect sound segments, they were used without voice and silence. Immediately, the results obtained in the experimental phase is disclosed, evaluate classifications using the confusion matrix of the 10 phrases in 65 words were about 40 segments of silence. Finally, conclusions and future work are described. Clearly that shows that the algorithms have not been implemented show overall efficiency in detecting voice activity in Spanish of the Rio de la Plata. We also found that the algorithms implemented using linear prediction coefficients show better performance.
We study the existence and uniqueness of Nash equilibria for a certain class of aggregative games with finite and possibly large number of players. Sufficient conditions for these are obtained using the theory of vari...
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We study the existence and uniqueness of Nash equilibria for a certain class of aggregative games with finite and possibly large number of players. Sufficient conditions for these are obtained using the theory of variational inequalities together with the specific structure of the objective functions. We further present an algorithm that converges to the Nash equilibrium in a decentralized fashion with provable guarantees. The theoretical results are applied to the problem of managing the charging of a large fleet of plug-in electric vehicles and the results are compared with the existing work.
This paper presents a target localization problem based on the time difference of arrival (TDOA) measurements by employing an improved genetic algorithm (GA) for estimation. The weighted least square (WLS) technique i...
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ISBN:
(纸本)9781509040872
This paper presents a target localization problem based on the time difference of arrival (TDOA) measurements by employing an improved genetic algorithm (GA) for estimation. The weighted least square (WLS) technique is applied as an efficient existing approach. The TDOA target localization problem is formulated as an optimization problem, with a highly nonlinear and multimodal objective function. The hybrid Genetic algorithm - Newton-Raphson (GA-NR) has been proposed as high accuracy and global convergence algorithm in this sense. Finally, the simulation results of the proposed optimization method show a significant performance improvement over existing WLS approach.
In this paper we have presented a detail on masking which is a technique used to resist power analysis attacks. Some of the works that have been done using this technique have been analyzed. Also, a masking based appr...
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ISBN:
(纸本)9781509000838
In this paper we have presented a detail on masking which is a technique used to resist power analysis attacks. Some of the works that have been done using this technique have been analyzed. Also, a masking based approach for RSA has been proposed. The proposed works first masks the plaintext and then execute the RSA instructions. In between, some real time random instructions were also inserted which not only masked the values but also could provide variations in the power consumptions thus resisting power analysis attacks.
Recently, the sustainability of traditional technologies employed in critical infrastructure brings a serious challenge for our society. In order to make decisions related with safety of critical infrastructure, the v...
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ISBN:
(纸本)9781509036660
Recently, the sustainability of traditional technologies employed in critical infrastructure brings a serious challenge for our society. In order to make decisions related with safety of critical infrastructure, the values of accidental risk are becoming relevant points for discussion. However the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and deal with high amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI). Therefore, this paper aims to investigate and compare AI algorithms for risk assessment. These algorithms are classified mainly into Expert Systems, Artificial Neural Networks and Hybrid intelligent Systems. This paper explains the principles of each classification system, as well as its applications in safety. Lately, this paper performs a comparative analysis of three representative techniques, such as Fuzzy-Expert System, Neural Networks, and Adaptive Neuro Fuzzy Inference System.
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