Using an FPGA coprocessor for improving execution speed of TRT-LUT

2004 ◽  
Author(s):  
C. Hinkelbein ◽  
A. Khomich ◽  
A. Kugel ◽  
R. Männer ◽  
M. Müller
Keyword(s):  
Execution Speed

The influence of execution speed on blood lactate concentration in strength training protocol in bench press exercise

2020 ◽  
Vol 19 (1) ◽  
pp. 32
Author(s):  
Gustavo Taques Marczynski ◽  
Luís Carlos Zattar Coelho ◽  
Leonardo Emmanuel De Medeiros Lima ◽  
Rodrigo Pereira Da Silva ◽  
Dilmar Pinto Guedes Jr ◽  
...  
Keyword(s):  
Strength Training ◽  
Blood Lactate ◽  
Bench Press ◽  
Lactate Concentration ◽  
Blood Lactate Concentration ◽  
The Third ◽  
Fast Speed ◽  
Work Volume ◽  
Execution Speed ◽  
Training Protocol

The aim of this study was to analyze the influence of two velocities of execution relative to blood lactate concentration in strength training exercise until the momentary concentric failure. Fifteen men (29.1 ± 5.9 years), trained, participated in the experiment. The volunteers performed three bench press sessions, with an interval of 48 hours between them. At the first session, individuals determined loads through the 10-12 RMs test. In the following two sessions, three series with 90 seconds of interval were performed, in the second session slow execution speed (cadence 3030) and later in the third session fast speed (cadence 1010). For statistical analysis, the Student-T test was used for an independent sample study and considered the value of probability (p) ≤ 0.05 statistically significant. By comparing the number of repetitions and time under tension of the two runs, all series compared to the first presented significant reductions (p < 0.05). The total work volume was higher with the fast speed (p < 0.05). The study revealed that rapid velocities (cadence 1010) present a higher concentration of blood lactate when compared to slow runs (cadence 3030). The blood lactate concentration, in maximum repetitions, is affected by the speed of execution.Keywords: resistance training, cadence, blood lactate.

ANALYZING AND IMPROVING WEB APPLICATION QUALITY USING DESIGN PATTERNS

2012 ◽  
Vol 2 (2) ◽  
pp. 112-116
Author(s):  
Shikha Bhatia ◽  
Mr. Harshpreet Singh
Keyword(s):  
Web Application ◽  
Design Pattern ◽  
Design Patterns ◽  
Web Applications ◽  
Past Research ◽  
Software Systems ◽  
Interactive Software ◽  
Web Based ◽  
Software Application ◽  
Execution Speed

With the mounting demand of web applications, a number of issues allied to its quality have came in existence. In the meadow of web applications, it is very thorny to develop high quality web applications. A design pattern is a general repeatable solution to a generally stirring problem in software design. It should be noted that design pattern is not a finished product that can be directly transformed into source code. Rather design pattern is a depiction or template that describes how to find solution of a problem that can be used in many different situations. Past research has shown that design patterns greatly improved the execution speed of a software application. Design pattern are classified as creational design patterns, structural design pattern, behavioral design pattern, etc. MVC design pattern is very productive for architecting interactive software systems and web applications. This design pattern is partition-independent, because it is expressed in terms of an interactive application running in a single address space. We will design and analyze an algorithm by using MVC approach to improve the performance of web based application. The objective of our study will be to reduce one of the major object oriented features i.e. coupling between model and view segments of web based application. The implementation for the same will be done in by using .NET framework.

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

Recursion: A choice between readability and execution speed

1986 ◽  
Vol 21 (5) ◽  
pp. 82-90 ◽  
Author(s):  
J F Watson
Keyword(s):  
Execution Speed

A Quasi-Dimensional Fuel Distribution Model for a Radially Stratified Engine

2021 ◽  
Author(s):  
Dennis Robertson ◽  
Patrick O'Donnell ◽  
Benjamin Lawler ◽  
Robert Prucka
Keyword(s):  
Reference Model ◽  
Computation Time ◽  
Distribution Model ◽  
Strategy Development ◽  
Real Time Control ◽  
Spray Penetration ◽  
Fuel Distribution ◽  
Time Control ◽  
Fuel Stratification ◽  
Execution Speed

Abstract Several combustion strategies leverage radial fuel stratification to adapt combustion performance between the center of the chamber and the outer regions independently. Spark-assisted compression ignition (SACI) relies on careful tuning of this radial stratification to maximize the combined performance of flame propagation and autoignition. Established techniques for determining in-cylinder fuel stratification are computationally intensive, limiting their feasibility for control strategy development and real-time control. A simplified model for radial fuel stratification is developed for control-oriented objectives. The model consists of three submodels: spray penetration, fuel distribution along the spray axis, and post-injection mixing. The spray penetration model is adapted from fuel spray models presented in the literature. The fuel distribution and mixing submodels are validated against injection spray results from an LES 3-D computational fluid dynamics (CFD) reference model for three test points as a function of crank angle. The quasi-one-dimensional model matches the CFD results with a root mean square error (RMSE) for equivalence ratio of 0.08?0.11. This is a 50% reduction from the 0.16?0.20 RMSE for a model that assumes a uniform fuel distribution immediately after injection. The computation time is 230 ms on an Intel Xeon E5-1620 v3 to solve each case without significant optimization for code execution speed.

scholarly journals Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1463-1468
Keyword(s):  
Assembly Language ◽  
Optimization Method ◽  
Program Optimization ◽  
Low Level ◽  
Code Performance ◽  
Execution Speed ◽  
The Difference ◽  
And Performance ◽  
High Level ◽  
Optimization And Performance

Software program optimization for improved execution speed can be achieved through modifying the program. Programs are usually written in high level languages then translated into low level assembly language. More coverage of optimization and performance analysis can be performed on low level than high level language. Optimization improvement is measured in the difference in program execution performance. Several methods are available for measuring program performance are classified into static approaches and dynamic approaches. This paper presents an alternative method of more accurately measuring code performance statically than commonly used code analysis metrics. New metrics proposed are designed to expose effectiveness of optimization performed on code, specifically unroll optimizations. An optimization method, loop unroll is used to demonstrate the effectiveness of the increased accuracy of the proposed metric. The results of the study show that measuring Instructions Performed and Instruction Latency is a more accurate static metric than Instruction Count and subsequently those based on it.

scholarly journals A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 1774-1778
Author(s):  
Mahesh Singh
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Real World ◽  
Research Paper ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Great Success ◽  
The Real ◽  
Road Traffic Safety ◽  
Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

&lt;p&gt;Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.&lt;/p&gt;&lt;p&gt;Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.&lt;/p&gt;&lt;p&gt;We will test and compare the different algorithms and evaluate the algorithms&amp;#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.&lt;/p&gt;&lt;p&gt;The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&amp;#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., &amp; Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. &lt;em&gt;The Cryosphere&lt;/em&gt;, &lt;em&gt;14&lt;/em&gt;(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.&lt;/p&gt;

Hearing Reaction Time Measurement Concentration Level Tool Integrated Android Smartphone

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhamad Arif Indiarto ◽  
Syamsudduha Syahrorini
Keyword(s):  
Reaction Time ◽  
Power Supply ◽  
High Performance ◽  
Concentration Level ◽  
Time Measurement ◽  
Push Button ◽  
High Productivity ◽  
Measurement Results ◽  
Reaction Time Measurement ◽  
Execution Speed

Performance in a job is very important because it will have an impact on the assessment and productivity of an employee, one of the indicators for evaluating high performance is related to concentration, execution speed and high productivity of the employee. Concentration is needed in working to prevent fatal accidents. In this study, it is possible to monitor measurement results via a smartphone, namely by using the Bluetooth HC-05 sensor as an integration to a smartphone. With 8 pushbutton, Arduino UNO microncontroller, Bluetooth HC-05, 16x2 LCD, and Buzzer. This tool works alternately when the push button Start is pressed, the power from the power supply will provide an electric current to the microncontroller, and continue to be connected to the Bluetooth HC-05, then by providing pushbuttons pressing input. Each pressing instruction on the pushbutton provides a different sound output, consisting of sound output, High, Mid, and Low. And continue on the LCD, and can display the results of the input that has been processed by the microcontroller. The output results are in the form of the amount of time displayed on the LCD, the sound from the buzzer, and from a series of work tools and the output results can be monitored via android smartphone. The results of this study are the accuracy of the tool in each variable low 99%, mid 90%, high 92%. The average tool ranges from 2.44. The error is low 7,4%, mid 7,4%, high 7,6%.

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