Sea clutter power reduction in radar measurement systems by feedforward multilayer perceptrons with medium input data integration rate

2009 ◽  
Author(s):  
R. Vicen-Bueno ◽  
R. Carrasco-Alvarez ◽  
M. Rosa-Zurera ◽  
J.C. Nieto-Borge
Keyword(s):  
Data Integration ◽  
Input Data ◽  
Power Reduction ◽  
Multilayer Perceptrons ◽  
Sea Clutter ◽  
Radar Measurement ◽  
Measurement Systems

scholarly journals TMaR: a two-stage MapReduce scheduler for heterogeneous environments

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Neda Maleki ◽  
Hamid Reza Faragardi ◽  
Amir Masoud Rahmani ◽  
Mauro Conti ◽  
Jay Lofstead
Keyword(s):  
Task Scheduling ◽  
Network Traffic ◽  
Input Data ◽  
Power Reduction ◽  
Heterogeneous Environments ◽  
Two Stage ◽  
Finish Time ◽  
Remote Storage ◽  
Simulation Results

Abstract In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.

scholarly journals Photogrammetric measurement system generating algorithm by primary input data

2021 ◽  
pp. 82-89
Author(s):  
Алексей Николаевич Самойлов ◽  
Юрий Михайлович Бородянский ◽  
Александр Валерьевич Волошин
Keyword(s):  
Information Technology ◽  
Measurement System ◽  
Input Data ◽  
Metallurgical Industry ◽  
Measuring System ◽  
Adjustment Process ◽  
Traditional Methods ◽  
Primary Input ◽  
Measurement Systems ◽  
Photogrammetric Measurement

В процессе автоматизации решения прикладных измерительных задач, в том числе на базе фотограмметрических методов, возникает проблема соответствия измерительной системы объекту и условиям измерения. Для того чтобы измерительная система позволяла заранее оценить возможность получения достоверных результатов, а также наилучшим образом подстраивалась под условия измерения, необходимо наличие специализированных алгоритмов и моделей. В общем случае такие модели ориентированы на квалифицированных технических специалистов, обладающих необходимыми знаниями в области информационных технологий. Особенностью применения фотограмметрических измерительных систем в лесной и металлургической промышленности является низкая квалификация пользователей в сфере информационных технологий, что обуславливается характером выполняемых работ и условиями привлечения. Данный фактор не позволяет решить задачу подстройки системы традиционными методами, в которых процессом настройки управляет пользователь. В этой связи в статье предлагается модель и алгоритм формирования измерительной системы по первичным входным данным, в котором процессом настройки управляет сама система. In the process of automating the solution of applied measurement tasks, including on the basis of photogrammetric methods, there is a problem of compliance of the measurement system with the object and measurement conditions. In order for the measuring system to assess in advance the possibility of obtaining reliable results, as well as to best adapt to the conditions of measurement, it is necessary to have specialized algorithms and models. In general, such models are aimed at qualified technicians with the necessary knowledge in the field of information technology. A feature of the application of photogrammetric measurement systems in the forestry and metallurgical industry is the low qualification of users in the field of information technology, which is determined by the nature of the work performed and the conditions of attraction. This factor does not solve the problem of adjusting the system by traditional methods in which the user controls the configuration process. In this regard, the article proposes a model and algorithm for forming a measuring system from primary input data, in which the system itself controls the adjustment process.

Sea Clutter Power Reduction in Pulse Forward Scatter Radar

2018 ◽  
Author(s):  
M. B. Porfido ◽  
M. Martorella ◽  
M. Gashinova ◽  
M. Cherniakov
Keyword(s):  
Power Reduction ◽  
Sea Clutter ◽  
Forward Scatter ◽  
Scatter Radar

Application of a laser ranging system to the metrologic certification of satellite radar measurement systems

1993 ◽  
Vol 36 (5) ◽  
pp. 524-525 ◽  
Author(s):  
N. N. Arev ◽  
B. F. Gorbunov ◽  
G. V. Pugachev ◽  
Yu. A. Bazlov
Keyword(s):  
Laser Ranging ◽  
Radar Measurement ◽  
Measurement Systems ◽  
Satellite Radar

Data integration using statistical matching techniques: A review

2021 ◽  
pp. 1-20
Author(s):  
Israa Lewaa ◽  
Mai Sherif Hafez ◽  
Mohamed Ali Ismail
Keyword(s):  
Data Integration ◽  
Input Data ◽  
Data File ◽  
Future Research ◽  
Multiple Sources ◽  
Statistical Tool ◽  
Statistical Matching ◽  
Data Files ◽  
Matching Techniques ◽  
Different Sources

In the era of data revolution, availability and presence of data is a huge wealth that has to be utilized. Instead of making new surveys, benefit can be made from data that already exists. As enormous amounts of data become available, it is becoming essential to undertake research that involves integrating data from multiple sources in order to make the best use out of it. Statistical Data Integration (SDI) is the statistical tool for considering this issue. SDI can be used to integrate data files that have common units, and it also allows to merge unrelated files that do not share any common units, depending on the input data. The convenient method of data integration is determined according to the nature of the input data. SDI has two main methods, Record Linkage (RL) and Statistical Matching (SM). SM techniques typically aim to achieve a complete data file from different sources which do not contain the same units. This paper aims at giving a complete overview of existing SM methods, both classical and recent, in order to provide a unified summary of various SM techniques along with their drawbacks. Points for future research are suggested at the end of this paper.

scholarly journals A Modified Signal Feed-Through Pulsed Flip- Flop for Low Power Applications

2017 ◽  
Vol 63 (3) ◽  
pp. 241-246 ◽  
Author(s):  
Ehsan Panahifar ◽  
Alireza Hassanzadeh
Keyword(s):  
Low Power ◽  
Input Data ◽  
Power Reduction ◽  
Leakage Power ◽  
Flip Flop ◽  
Dynamic Power ◽  
Pass Transistor ◽  
Power Delay Product ◽  
Hspice Simulation

AbstractIn this paper a modified signal feed-through pulsed flip-flop has been presented for low power applications. Signal feed-through flip-flop uses a pass transistor to feed input data directly to the output. Feed through transistor and feedback signals have been modified for delay, static and dynamic power reduction. HSPICE simulation shows 22% reduction in leakage power and 8% of dynamic power. Delay has been reduced by 14% using TSMC 90nm technology parameters. The proposed pulsed flip-flop has the lowest PDP (Power Delay Product) among other pulsed flip-flops discussed.

scholarly journals Formation of the Convergence Functions of Errors of Input Data of Measurement Systems Computing Components on the Basis of the Finite Automatics Theory

2019 ◽  
pp. 37-40
Author(s):  
O. Krychevets
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Input Data ◽  
Transformation Function ◽  
Measurement Systems ◽  
Data Errors ◽  
Measurement Results ◽  
Data Error ◽  
Iterative Procedures ◽  
Number Of Iterations

This paper presents the results of an investigation into the behavior of the functions of transforming the input data errors for different types of measurement systems’ computing components in order to use their generalized models developed on the basis of the finite automata theory. It is shown that, depending on the kind and value of an input data error transformation function (metrological condition of computing components), the errors of measurement results obtained with the systems’ measuring channels have a determinate character of changes in both static and dynamic regimes of computing components. Determined are the basic dependences of the errors of measurement results upon the input data errors, and upon the types of input data transformation functions; given are the results of their calculation. The investigation results demonstrate a linear character of the dependence of measurement result errors upon the input data errors ΔХ{(tn). In addition, the transformation function calculation f = ΔY{(tn)/ΔХ{(tn) gives its steady state value f = 1,0, i.e. a computing component does not transform the input data error, and does not reverse its sign. For the iterative procedures, the input data errors do not affect the final measurement result, and its accuracy. The measurement error values Δуn depend on the iteration number, and decrease with the increasing number. Of particular interest is the behavior of the function of transforming the input data errors: first, its values are dependent upon the number of iterations; second, f < 1, which clearly shows that the input data errors decrease with the increa­sing number of iterations; and third, the availability of values f = 0 indicates that the function of transforming the input data errors is able to «swallow up» the input data error at the end of the computational procedure. For the linear-chain structures, data have been obtained for a predominantly linear dependence of the measurement error Δs on the input data error Δх, and for the absence of the chain’s transformation function f dependence on the input data errors Δх. For the computing components having a cyclic structure, typi­cal is the same dependence of measurement errors Δt on the input data errors and on the behavior of transformation function ft/x which are specific to the above mentioned computing components that rea­lize iterative procedures. The difference is that the computing components having a cyclic structure realize the so-called (sub)space iteration as opposed to the time iteration specific to the computing components considered. The computing components having a complicated structure (e.g. serial-cyclic, serial-parallel, etc.) demonstrate the dependence of measurement errors on the input data errors which is specific to the linear link that, with such a structure, is determinative for eva­luating the measurement error. Also the function of transforming the input data errors behaves similarly.

An Adaptive Lattice Architecture for Dynamic Multilayer Perceptrons

1992 ◽  
Vol 4 (6) ◽  
pp. 922-931 ◽  
Author(s):  
Andrew D. Back ◽  
Ah Chung Tsoi
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Input Data ◽  
Learning Algorithm ◽  
Nonlinear Approximation ◽  
Infinite Impulse Response ◽  
Multilayer Perceptrons ◽  
Time Series Modeling ◽  
Input Signals

Time-series modeling is a topic of growing interest in neural network research. Various methods have been proposed for extending the nonlinear approximation capabilities to time-series modeling problems. A multilayer perceptron (MLP) with a global-feedforward local-recurrent structure was recently introduced as a new approach to modeling dynamic systems. The network uses adaptive infinite impulse response (IIR) synapses (it is thus termed an IIR MLP), and was shown to have good modeling performance. One problem with linear IIR filters is that the rate of convergence depends on the covariance matrix of the input data. This extends to the IIR MLP: it learns well for white input signals, but converges more slowly with nonwhite inputs. To solve this problem, the adaptive lattice multilayer perceptron (AL MLP), is introduced. The network structure performs Gram-Schmidt orthogonalization on the input data to each synapse. The method is based on the same principles as the Gram-Schmidt neural net proposed by Orfanidis (1990b), but instead of using a network layer for the orthogonalization, each synapse comprises an adaptive lattice filter. A learning algorithm is derived for the network that minimizes a mean square error criterion. Simulations are presented to show that the network architecture significantly improves the learning rate when correlated input signals are present.

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