scholarly journals Increasing quality of the wireless module for monitoring and supervision of sound series of the expanded purpose

2021 ◽  
Vol 6 (5 (114)) ◽  
Author(s):  
Alexandr Trunov ◽  
Zhan Byelozyorov
Keyword(s):  
Time Series Data ◽  
Voltage Drop ◽  
Series Data ◽  
Software Interfaces ◽  
Arduino Uno ◽  
Computerized Monitoring ◽  
Different Origins ◽  
Wireless Module ◽  
Receiving Module

The sound series are considered as an addition to visual and thermal imaging information flows when using computerized monitoring systems (CS). A minimum complete structure of spaced microphones for collecting data on sound rows, which is suitable for calibrating, isolating and transmitting data on sound anomalies (SA), is proposed.  Duplication of the data transmission channel by wire and Wi-Fi module for recording and determining the type and coordinates of the SA is provided. An experimental receiving module has been assembled, which includes microphones, amplifiers and signals matching boards for digital and analog forms, an ARDUINO UNO WIFI REV2 controller with an integrated Wi-Fi module. It is presented that its addition with a personal computer and a smartphone with the Android operating system forms a CS for remote wireless control of the course of the experimental analysis of sound series. It has been confirmed experimentally that its structure is minimally complete. An algorithm was developed and a software package was written in C/C++ languages. It is shown that the number of microphones is selected from the conditions of the problem from 1 to 5, but their number is limited to five digital inputs of the ARDUINO UNO WIFI REV2 board. A wave representation of the law of temporal changes in intensity and the integral norm of SA is applied. The possibilities of calibrating all data of sound series in analog and digital form are demonstrated. The article presents the suitability of testing the algorithms for determining the phases of echograms from time series data, containing SAs of different origins and recorded by three different microphones. The effect of connecting a Wi-Fi module on reducing the voltage drop by 0.5–1 V is shown. The necessity of an additional registration condition for all microphones is demonstrated. The software interfaces for the calibration of the receiving module and the operation of the mobile application have been developed.

scholarly journals Growth Limits: A Conceptual Analysis for Sustainable Development in Nigeria

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402091827
Author(s):  
Oluwabunmi O. Adejumo
Keyword(s):  
Time Series Data ◽  
Optimal Growth ◽  
Series Data ◽  
Data Sets ◽  
African Countries ◽  
Limits To Growth ◽  
Development Indicators ◽  
Long Run ◽  
Sub Saharan

In the school of development thought, growth has been identified as a viable alternative to the challenge of poverty and economic backwardness. However, the ecologists have continuously challenged the growth position in relation to environmental degradation and depletion. It is against this background; this study examined the limits to growth in Nigeria beyond which there will be inimical consequences for the environment. The study employed time series data that spanned between 1970 and 2014. These data sets were sourced from the World Development Indicators. Based on the assimilation model, threshold estimates were used to identify optimal growth regions, whereas regression estimates were used to measure growth effects. It was discovered that below the identified growth limit, there are currently significant negative impacts on the quality of the environment in Nigeria via economic growth. This study is a single-country case, that is, Nigeria; hence, the study can be expanded to include other sub-Saharan African countries. The study adds to knowledge by establishing the prospects for sustainability in the quality of the environment in the long run; therefore, policies designed in this areas have higher likelihood of attaining sustainability.

scholarly journals Accounting Implications of Micro-Fiscal Measures and Quality of Real Gross National Goods and Services: Empirical Evidence From Nigeria

2020 ◽  
Vol 11 (6) ◽  
pp. 155
Author(s):  
Akabom I. Asuquo ◽  
Arzizeh Tiesieh Tapang ◽  
Uwem E. Uwah ◽  
Nicholas O. Dan ◽  
Ashishie Peter Uklala
Keyword(s):  
Empirical Evidence ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Exploratory Research ◽  
Direct Effects ◽  
Goods And Services ◽  
The Government

The study explored into accounting implications of micro-fiscal measures and quality of real gross national goods and services: empirical evidence from Nigeria for a period of thirty years. The objective was to examine how micro-fiscal measures affect real gross national goods and services using thirty years’ time-series data. The exploratory research methodology was applied and data collected were analysed using multiple regression and other statistical techniques. Findings of the study revealed that significant and direct effects were exerted on gross national goods and services by all the known and identified micro-fiscal measures in the review, except swap and levy ratios which had inverse relationship as revealed by their coefficients obtained from the analysis. Therefore, the government and government agencies have a duty to control macro-fiscal activities in terms of creation of national goods, wealth and services using the identified micro-fiscal mechanisms as the basis for decisions and policies making besides implementation.

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

scholarly journals The Role of Cooperatives, Remittances, and Infrastructure in Export Performance of Nepal: ARDL Approach of Cointegration

2020 ◽  
pp. 151-159
Author(s):  
Ramesh C. Paudel ◽  
Chakrapani Acharya ◽  
Resham Thapa-Parajuli
Keyword(s):  
Time Series Data ◽  
Export Performance ◽  
Series Data ◽  
Supply Side ◽  
Policy Makers ◽  
Ardl Approach ◽  
Negative Role ◽  
Supply Side Factors

Cooperatives, remittances, and foreign direct investment (FDI) are crucial source of funds required for better entrepreneurships, which combinedly along with the quality of infrastructure can contribute to enhance the supply side factors of the export performance. Due to the well perceived role of cooperatives, Nepal’s constitution 2015 mentions this sector as one of the three pillars of the national economy while around 30 percent of Nepal’s GDP comes from remittances. As the country lacks the domestic sources for investment, FDI has become an indispensable part of the development sources of the developing countries in the recent decades. This paper analyzes the role of cooperatives, remittances, FDI and infrastructure in export performance of Nepal using the Autoregressive Distributive Lag (ARDL) approach of cointegration as suggested by the properties of the time series data for the period of 26 years from 1993 to 2018. The major finding shows that the cooperatives have not contributed to export performance as expected, however the role is positive. The remittances have a strong negative role on export performance, which is largely impacted by the number and quality of the infrastructure. The role of FDI is also negative and might be due to insufficient volume to contribute substantially. This fact seeks the urgent attention from the policy makers to make the country more investment friendly.

scholarly journals A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

2020 ◽  
Vol 12 (18) ◽  
pp. 2888 ◽  
Author(s):  
Nishanta Khanal ◽  
Mir Abdul Matin ◽  
Kabir Uddin ◽  
Ate Poortinga ◽  
Farrukh Chishtie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Input Image ◽  
Series Data ◽  
Data Set ◽  
Temporal Smoothing ◽  
Land Cover Data ◽  
Cover Class

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.

scholarly journals Inferring phage–bacteria infection networks from time-series data

2016 ◽  
Vol 3 (11) ◽  
pp. 160654 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage–bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage–bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage–bacteria pair individually. We use in silico experiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.

ASSESSMENT OF THE QUALITY OF TIME-SERIES DATA

Epidemiology ◽  
1996 ◽  
Vol 7 (Supplement) ◽  
pp. S69
Author(s):  
S TONG
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

scholarly journals Radial-Based Noise Power Estimation for Weather Radars

2013 ◽  
Vol 30 (12) ◽  
pp. 2737-2753 ◽  
Author(s):  
Igor R. Ivić ◽  
Christopher Curtis ◽  
Sebastián M. Torres
Keyword(s):  
Real Time ◽  
Time Series Data ◽  
Internal Noise ◽  
Radar Data ◽  
Meteorological Variables ◽  
Series Data ◽  
Noise Power ◽  
System Noise ◽  
Antenna Position

Abstract A radar antenna intercepts thermal radiation from various sources including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low-to-moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. In this paper, an effective method is proposed to estimate the noise power in real time from measured powers at each radial. The technique uses a set of criteria to detect radar range resolution volumes that do not contain weather signals and uses those to estimate the noise power. The algorithm is evaluated using both simulated and real time series data; results show that the proposed technique accurately produces estimates of the system noise power. An operational implementation of this technique is expected to significantly improve the quality of weather radar products with a relatively small computational burden.

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