Efficiency Comparison of Modern Computer Languages: Sorting Benchmark

2018 ◽  
pp. 299-310
Author(s):  
Agnieszka Bier ◽  
Zdzisław Sroczyński
Keyword(s):  
Computer Languages ◽  
Modern Computer ◽  
Efficiency Comparison

Applications of a Decomposed Analysis Procedure for Area-Array Packages

2000 ◽  
Vol 123 (2) ◽  
pp. 132-140
Author(s):  
Terrace B. Thompson ◽  
Ganesh Subbarayan
Keyword(s):  
Mechanical Energy ◽  
Three Dimensional ◽  
Optimization Procedure ◽  
Computer Code ◽  
Analysis Procedure ◽  
Object Oriented Programming ◽  
Electronic Packages ◽  
Computer Languages ◽  
Modern Computer ◽  
Independent Analysis

The goals of the present paper are to apply the recently developed decomposed analysis procedure using a computer code developed in this study. The decomposed technique enables one to determine the equilibrium configuration of electronic packages with significant computational efficiency at a reasonable accuracy. Further, it allows the independent analysis of the subsystems enabling “reusable” modules in a manner analogous to the object-oriented programming paradigm of modern computer languages. The code described here uses a nonlinear optimization procedure that ensures the approximate satisfaction of the balance of mechanical energy. The developed procedure is demonstrated on a variety of two- and three-dimensional hypothetical and “real-world” electronic packages. It is shown that with the use of the decomposed solution methodology, for a 225 I/O PBGA package, a speedup of nearly seven times is achieved at an accuracy loss in displacements of approximately 5.5 percent. It is also shown that the calculated peak shear displacements agree very well with experimental measurements made using laser moire´ interferometry. Since the analysis procedure is independent of the number of solder interconnects, significantly larger time savings are expected for larger packages.

scholarly journals qtcm 0.1.2: a Python implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation Model

2009 ◽  
Vol 2 (1) ◽  
pp. 1-11 ◽  
Author(s):  
J. W.-B. Lin
Keyword(s):  
Climate Models ◽  
Model Performance ◽  
Circulation Model ◽  
Fast Time ◽  
Quasi Equilibrium ◽  
Tropical Circulation ◽  
Computer Languages ◽  
Modern Computer ◽  
Model Execution ◽  
Run Time

Abstract. Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

scholarly journals qtcm 0.1.2: A Python Implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation model

2008 ◽  
Vol 1 (1) ◽  
pp. 315-344 ◽  
Author(s):  
J. W.-B. Lin
Keyword(s):  
Climate Models ◽  
Model Performance ◽  
Circulation Model ◽  
Fast Time ◽  
Quasi Equilibrium ◽  
Tropical Circulation ◽  
Computer Languages ◽  
Modern Computer ◽  
Model Execution ◽  
Run Time

Abstract. Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

scholarly journals Modern simulation utilities for genetic analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sarah S. Ji ◽  
Christopher A. German ◽  
Kenneth Lange ◽  
Janet S. Sinsheimer ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Linear Models ◽  
Ease Of Use ◽  
Reproducible Research ◽  
Easy Access ◽  
Diagnostic Tools ◽  
Integrated Analysis ◽  
Integrated Simulation ◽  
Computer Languages ◽  
Modern Computer ◽  
Study Designs

Abstract Background Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools. Results We present , an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research. Conclusion The package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.

Data Security in Health Information Systems by Applying Software Techniques

1979 ◽  
Vol 18 (04) ◽  
pp. 214-222
Author(s):  
K. Sauter
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Data Security ◽  
Health Information Systems ◽  
Database Systems ◽  
Modern Computer ◽  
Proper Design ◽  
Software Construction ◽  
Organizational Procedures ◽  
Software Measures

The problems encountered in achieving data security within computer-supported information systems increased with the development of modern computer systems. The threats are manifold and have to be met by an appropriate set of hardware precautions, organizational procedures and software measures which are the topic of this paper. Design principles and software construction rules are treated first, since the security power of a system is considerably determined by its proper design. A number of software techniques presented may support security mechanisms ranging from user identification and authentication to access control, auditing and threat monitoring. Encryption is a powerful tool for protecting data during physical storage and transmission as well.Since an increasing number of health information systems with information-integrating functions are database-supported, the main issues and terms of database systems and their specific security aspects are summarized in the appendix.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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