scholarly journals PENGARUH JARAK TERHADAP KUALITAS GAMBAR DALAM PENGIRIMAN CITRA DIGITAL MELALUI JARINGAN WIRELESS PADA KAMERA LS Y201

Compiler ◽  
2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Zhulfa Arif Hidayat ◽  
Denny Dermawan ◽  
Nurcahyani Dewi Retnowati
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Digital Image ◽  
High Frequency ◽  
Digital Images ◽  
Test Results ◽  
Capture Process ◽  
Ultra High Frequency ◽  
Spot Area ◽  
Test Result

Digital image was needed us a medium of information that can be transmitted by cable or wireless media. To obtain digital images must use a tool such as a camera. Users can use the camera to get a digital image with the remote sensing method on an object in a particular place. In the daily activities, users can take advantages of the digital image (pictures or video) that are useful for media documentation, monitoring system in somewhere and others. The design of this tool using LS_Y201camera to capture a digital image and wireless as a data transmission media. In this case a wireless media use Ultra High Frequency transmitter and receiver that support for remote sensing. Users run the tool through an application that is connected with a wireless media. This application is designed byDelphi7. Applications and wireless camera was made for simulation media of remote sensing and monitoring system in the blank spot area. The test result of applications and tools that use the Ultra High Frequency (wireless), can be viewed from a computer interface. In this case, the signal strength ofthe transmitter greatly affect the maximum distance that can be taken to make capture process. The test results are as follows: the best results at a distance of 10 meters = 011110102 (12210); distance of 20 meters = 011100112 (11510); distance of 30 meters = 011110102 (12210); distance of 40 meters =011011112 (11110); distance of 50 meters = 011100102 (11410). So the best distance to digital images transmission through a wireless networks are at a distance of 40 meters.

scholarly journals Application of matlab for digital images topological characteristics calculation and analysis

2021 ◽  
Vol 60 (1) ◽  
pp. 72-78
Author(s):  
Olga V. Samarina ◽  
Valeriy A. Samarin ◽  
Viktor V. Slavsky ◽  
Maria V. Kurkina
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Data Processing ◽  
Digital Image ◽  
Digital Images ◽  
Remote Sensing Data ◽  
Complete Information ◽  
Contour Lines ◽  
Topographic Characteristics ◽  
Topological Characteristics

The paper describes the practical results received from digital images topographic characteristics calculating in Matlab, such as length and curvature of contour lines, density of lengths and curvature, as well as irregularities of contour lines of the first and second order. Topological characteristics contain complete information about the shape and contours of a digital image, which allows them to be effectively used in solving the problems of remote sensing data processing, analysis of biomedical images, classification and pattern recognition problems.

scholarly journals Performance Analysis of Different Filters for Digital Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 2572-2576
Author(s):  
Ch. Kavya , Et. al.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Pattern Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Frequency ◽  
Satellite Remote Sensing ◽  
Low Frequency ◽  
Linear Filters

Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concerning digital image processing. Here linear, as well as non-linear filters, are presented. It is easy to decide about the better filter for improving the image processing output from the discussion.

Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator

2016 ◽  
pp. 1-34 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Frequency ◽  
Higher Order ◽  
Signal Generator ◽  
Control Signal ◽  
Test Results ◽  
Model Learning ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks

This chapter develops a new nonlinear model, Ultra high frequency Polynomial and Trigonometric Higher Order Neural Networks (UPT-HONN), for control signal generator. UPT-HONN includes UPS-HONN (Ultra high frequency Polynomial and Sine function Higher Order Neural Networks) and UPC-HONN (Ultra high frequency Polynomial and Cosine function Higher Order Neural Networks). UPS-HONN and UPC-HONN model learning algorithms are developed in this chapter. UPS-HONN and UPC-HONN models are used to build nonlinear control signal generator. Test results show that UPS-HONN and UPC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UPS-HONN and UPC-HONN models can generate control signals with error approaching 0.0000%.

Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator

2016 ◽  
pp. 648-681
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Frequency ◽  
Higher Order ◽  
Signal Generator ◽  
Control Signal ◽  
Test Results ◽  
Model Learning ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks

This chapter develops a new nonlinear model, Ultra high frequency Polynomial and Trigonometric Higher Order Neural Networks (UPT-HONN), for control signal generator. UPT-HONN includes UPS-HONN (Ultra high frequency Polynomial and Sine function Higher Order Neural Networks) and UPC-HONN (Ultra high frequency Polynomial and Cosine function Higher Order Neural Networks). UPS-HONN and UPC-HONN model learning algorithms are developed in this chapter. UPS-HONN and UPC-HONN models are used to build nonlinear control signal generator. Test results show that UPS-HONN and UPC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UPS-HONN and UPC-HONN models can generate control signals with error approaching 0.0000%.

Control Signal Generator Based on Ultra-High Frequency Polynomial and Trigonometric Higher Order Neural Networks

2021 ◽  
pp. 416-454
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Frequency ◽  
Higher Order ◽  
Signal Generator ◽  
Control Signal ◽  
Test Results ◽  
Model Learning ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks

This chapter develops a new nonlinear model, ultra high frequency polynomial and trigonometric higher order neural networks (UPT-HONN) for control signal generator. UPT-HONN includes UPS-HONN (ultra high frequency polynomial and sine function higher order neural networks) and UPC-HONN (ultra high frequency polynomial and cosine function higher order neural networks). UPS-HONN and UPC-HONN model learning algorithms are developed in this chapter. UPS-HONN and UPC-HONN models are used to build nonlinear control signal generator. Test results show that UPS-HONN and UPC-HONN models are better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UPS-HONN and UPC-HONN models can generate control signals with error approaching 10-6.

A Study on the System of Radio Frequency Identification and Localization Works in UHF

2012 ◽  
Vol 588-589 ◽  
pp. 932-935 ◽  
Author(s):  
Yong Pan ◽  
Kai Hua Liu ◽  
Yi Gao ◽  
Rui Zhao
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Frequency ◽  
Test Results ◽  
Actual Measurement ◽  
Ultra High Frequency ◽  
Master Controller ◽  
Frequency Identification ◽  
Uhf Band

This paper designs a hand-held radio frequency identification and localization terminal based on ARM Cortex-M3 processor and active radio frequency tags based on 8051 processor. This terminal uses STM32F103VET6 as its master controller, these tags use STC12C2052AD as their master controller. They all use Nordic nRF905 as their radio frequency identification module. The system works in 433M/868M/915MHz ISM UHF band and can read, write and locate radio frequency tags within 50m indoors. The actual measurement of active radio frequency tags shows that this terminal is highly stable and comparable in 433M/868M/915M ultra-high frequency radio identification and localization. The test results of this system are also analyzed and presented.

scholarly journals On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3271 ◽  
Author(s):  
Wojciech Sikorski ◽  
Krzysztof Walczak ◽  
Wieslaw Gil ◽  
Cyprian Szymczak
Keyword(s):  
Acoustic Emission ◽  
Monitoring System ◽  
High Frequency ◽  
Power Transformer ◽  
Partial Discharge ◽  
Power Transformers ◽  
Ultra High Frequency ◽  
On Line ◽  
Simultaneous Use ◽  
Dielectric Window

The article presents a novel on-line partial discharge (PD) monitoring system for power transformers, whose functioning is based on the simultaneous use of three unconventional methods of PD detection: high-frequency (HF), ultra-high frequency (UHF), and acoustic emission (AE). It is the first monitoring system equipped in an active dielectric window (ADW), which is a combined ultrasonic and electromagnetic PD sensor. The article discusses in detail the process of designing and building individual modules of hardware and software layers of the system, wherein the most attention was paid to the PD sensors, i.e., meandered planar inverted-F antenna (MPIFA), high-frequency current transformer (HFCT), and active dielectric window with ultrasonic transducer, which were optimized for detection of PDs occurring in oil-paper insulation. The prototype of the hybrid monitoring system was first checked on a 330 MVA large power transformer during the induced voltage test with partial discharge measurement (IVPD). Next, it was installed on a 31.5 MVA substation power transformer and integrated according to the standard IEC 61850 with SCADA (Supervisory Control and Data Acquisition) system registering voltage, active power, and oil temperature of the monitored unit. The obtained results showed high sensitivity of the manufactured PD sensors as well as the advantages of the simultaneous use of three techniques of PD detection and the possibility of discharge parameter correlation with other power transformer parameters.

Item Context and /s/ Phone Articulation Test Results

1972 ◽  
Vol 15 (4) ◽  
pp. 852-860 ◽  
Author(s):  
Zoe Zehel ◽  
Ralph L. Shelton ◽  
William B. Arndt ◽  
Virginia Wright ◽  
Mary Elbert
Keyword(s):  
Test Score ◽  
Sound Production ◽  
Broad Context ◽  
Test Results ◽  
Production Task ◽  
Test Result

Fourteen children who misarticulated some phones of the /s/ phoneme were tape recorded articulating several lists of items involving /s/. The lists included the Mc-Donald Deep Test for /s/, three lists similar to McDonald’s but altered in broad context, and an /s/ sound production task. Scores from lists were correlated, compared for differences in means, or both. Item sets determined by immediate context were also compared for differences between means. All lists were found to be significantly correlated. The comparison of means indicated that both broad and immediate context were related to test result. The estimated “omega square” statistic was used to evaluate the percentage of test score variance attributable to context.

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