12 research outputs found
Robust object detection in images corrupted by impulse noise
This paper proposes two effective normalized similarity functions for robust object detection in very high density impulse noisy images. These functions form an integral similarity estimate based on relations of minimum by maximum values for all pairs of analyzed image features. To provide invariance under the constant brightness changes, zero-mean additive modification is used. We explore properties of our functions and compare them with other commonly used for object detection in images corrupted by impulse noise. The efficiency of our approach is illustrated and confirmed by experimental results
Effective Object Localization in Images by Calculating Ratio and Distance Between Pixels
Bohush, Rykhard & Ablameyko, Sergey & Adamovsky, Egor. (2020). Effective Object Localization in Images by Calculating Ratio and Distance Between Pixels.In this paper, two novel similarity functions which consider the spatial and brightness relations between pixels for object localization in images are presented. We explore different advantages of our functions and compare them to others that use only spatial connection between pixels. It is shown, that one of them is robust to linear change in pixel brightness levels of the compared images. Comparison of computational cost and localization accuracy of shifted object for our similarity functions with others is given in the paper. The presented experimental results confirm the effectiveness of the proposed approach for object localization
Object detection algorithm for high resolution images based on convolutional neural network and multiscale processing
In this article we propose an effective algorithm for small object detection in high resolution images. We look at the image at different scales and use block processing by convolutional neural network. Pyramid layers number is defined by input image resolution and convolutional layer size. On each layer of pyramid except the highest we perform splitting overlapping blocks to improve small object detection accuracy. Detected areas are merged into one if they belong to the same class and have high overlapping value. In the paper experimental results using YOLOv4 for 4K and 8K images are presented. Our algorithm shows better detecting small objects results in high-definition video than YOLOv4
Model for building of the radio environment map for cognitive communication system based on LTE
Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΌΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠ° Π² ΡΠ΅Π»Π΅ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΡΡ
. ΠΠΊΡΠ΅Π½ΡΠΈΡΡΠ΅ΡΡΡ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅, ΡΡΠΎ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ°Π΄ΠΈΠΎ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΡΡΡΠΏΠ° ΠΊ ΡΠΏΠ΅ΠΊΡΡΡ, Π΄Π»Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΠΎΡΡΡ
Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌ Π±ΠΎΠ»ΡΡΠΎΠΉ ΠΎΠ±ΡΠ΅ΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΠΉ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ Π±Π°Π·ΠΎΠ²ΡΡ
ΡΡΠ°Π½ΡΠΈΠΉ ΠΈ Π°Π±ΠΎΠ½Π΅Π½ΡΠΎΠ² ΡΠ΅ΡΠΈ. Π₯ΡΠ°Π½Π΅Π½ΠΈΠ΅ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄ΠΎΠ»ΠΆΠ½Ρ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡΡΡ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΊΠ°ΡΡΡ ΡΠ°Π΄ΠΈΠΎΡΡΠ΅Π΄Ρ, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ Π±Π°Π·Ρ Π΄Π°Π½Π½ΡΡ
Π²ΡΠ΅Ρ
Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠ΅ΠΉ Π² ΡΠ΅ΡΠΈ ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π΄ΠΎΡΡΡΠΏΠ½ΡΠ΅ Π΄Π»Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π² Π·Π°Π΄Π°Π½Π½ΠΎΠ΅ Π²ΡΠ΅ΠΌΡ ΡΠ°ΡΡΠΎΡΡ. Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° Π΄Π²ΡΡ
ΡΡΠΎΠ²Π½Π΅Π²Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ°ΡΡΡ ΡΠ°Π΄ΠΈΠΎΡΡΠ΅Π΄Ρ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΎΡΠΎΠ²ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ LTE, Π² ΠΊΠΎΡΠΎΡΠΎΠΉ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΈ Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΡΠΉ ΡΡΠΎΠ²Π½ΠΈ, ΠΎΠΏΠΈΡΡΠ²Π°Π΅ΠΌΠ°Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ: Π½Π°Π±ΠΎΡ ΡΠ°ΡΡΠΎΡ, ΠΎΡΠ»Π°Π±Π»Π΅Π½ΠΈΠ΅ ΡΠΈΠ³Π½Π°Π»Π°, ΠΊΠ°ΡΡΠ° ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠ², ΡΠ°Π³ ΡΠ΅ΡΠΊΠΈ, ΡΠ΅ΠΊΡΡΠΈΠΉ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΎΡΡΡΠ΅Ρ. ΠΠ»ΡΡΠ΅Π²ΡΠΌΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠ°ΠΌΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ²Π»ΡΡΡΡΡ Π±Π°Π·ΠΎΠ²Π°Ρ ΡΡΠ°Π½ΡΠΈΡ ΠΈ Π°Π±ΠΎΠ½Π΅Π½ΡΡΠΊΠΎΠ΅ ΡΡΡΡΠΎΠΉΡΡΠ²ΠΎ. Π ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ Π±Π°Π·ΠΎΠ²ΠΎΠΉ ΡΡΠ°Π½ΡΠΈΠΈ ΠΎΡΠ½Π΅ΡΠ΅Π½Ρ: Π½Π°ΠΈΠΌΠ΅Π½ΠΎΠ²Π°Π½ΠΈΠ΅, ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡ, ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΡ ΡΡΠ΅ΠΉΠΊΠΈ, Π½ΠΎΠΌΠ΅Ρ, Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½, ΠΌΠΎΡΠ½ΠΎΡΡΡ ΠΈΠ·Π»ΡΡΠ΅Π½ΠΈΡ, Π½ΠΎΠΌΠ΅ΡΠ° ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½Π½ΡΡ
Π°Π±ΠΎΠ½Π΅Π½ΡΡΠΊΠΈΡ
ΡΡΡΡΠΎΠΉΡΡΠ², Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΈΠΌ ΡΠ΅ΡΡΡΡΠ½ΡΠ΅ Π±Π»ΠΎΠΊΠΈ. ΠΠ»Ρ Π°Π±ΠΎΠ½Π΅Π½ΡΡΠΊΠΈΡ
ΡΡΡΡΠΎΠΉΡΡΠ² Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ: Π½Π°ΠΈΠΌΠ΅Π½ΠΎΠ²Π°Π½ΠΈΠ΅, ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡ, ΠΌΠ΅ΡΡΠΎΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅, ΡΠ΅ΠΊΡΡΠΈΠ΅ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΡ ΡΡΠ΅ΠΉΠΊΠΈ ΡΡΡΡΠΎΠΉΡΡΠ²Π°, ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡ ΡΠ°Π±ΠΎΡΠ΅ΠΉ Π±Π°Π·ΠΎΠ²ΠΎΠΉ ΡΡΠ°Π½ΡΠΈΠΈ, ΡΠ°ΡΡΠΎΡΠ½ΡΠΉ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½, Π½ΠΎΠΌΠ΅ΡΠ° ΡΠ΅ΡΡΡΡΠ½ΡΡ
Π±Π»ΠΎΠΊΠΎΠ² Π΄Π»Ρ ΡΠ²ΡΠ·ΠΈ ΡΠΎ ΡΡΠ°Π½ΡΠΈΠ΅ΠΉ, ΠΌΠΎΡΠ½ΠΎΡΡΡ ΠΈΠ·Π»ΡΡΠ΅Π½ΠΈΡ, ΡΡΠ°ΡΡΡ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ Π΄Π°Π½Π½ΡΡ
, ΡΠΏΠΈΡΠΎΠΊ Π½ΠΎΠΌΠ΅ΡΠΎΠ² Π±Π»ΠΈΠΆΠ°ΠΉΡΠΈΡ
ΡΡΠ°Π½ΡΠΈΠΉ, ΡΠ°ΡΠΏΠΈΡΠ°Π½ΠΈΡ ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ΅Π°Π½ΡΠΎΠ² ΡΠ²ΡΠ·ΠΈ ΡΡΡΡΠΎΠΉΡΡΠ². ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π΄Π»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΡΠ΅Π½Π°ΡΠΈΠ΅Π² ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ΅Π°Π½ΡΠΎΠ² ΡΠ²ΡΠ·ΠΈ Π°Π±ΠΎΠ½Π΅Π½ΡΡΠΊΠΈΡ
ΡΡΡΡΠΎΠΉΡΡΠ². ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠ°ΡΡΠ΅ΡΠ° ΠΊΠ°ΡΡΡ ΡΠ°Π΄ΠΈΠΎΡΡΠ΅Π΄Ρ Π² ΡΠΎΡΠΊΠ΅ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΊΠΈ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠΈ ΡΠ°Π΄ΠΈΠΎΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΎΡ ΠΈΠ·Π»ΡΡΠ°ΡΡΠΈΡ
ΡΡΡΡΠΎΠΉΡΡΠ². ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ°ΠΊΠ΅ΡΠ° MatLab. ΠΠΏΠΈΡΠ°Π½Ρ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΠΏΠΎΠ²ΡΡΠΈΡΡ Π±ΡΡΡΡΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Π΅Π΅ ΡΠ°Π±ΠΎΡΡ. ΠΡΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π²ΡΠ±ΠΎΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΡΡ Ρ ΡΡΠ΅ΡΠΎΠΌ Π΄Π°Π½Π½ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠ²ΡΠ·ΠΈ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ². ΠΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ°ΡΡΡ ΡΠ°Π΄ΠΈΠΎΡΡΠ΅Π΄Ρ, ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡΠΈΠ΅ ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ
ΠΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΡΠ΅Π½ΠΊΠΈ Π·Π°ΡΠΈΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ°Π½Π°Π»Π° ΡΡΠ΅ΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ ΡΠ΅ΡΠ΅Π²ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°
A method of information leakage channel security estimating based on the test speech signal envelope cross-correlation analysis is proposed and its includes: test signal generating and extracting its envelope, emitting and measuring in a leakage channel, extracting the resulting signal envelope, calculating the correlation coefficient between the original and received envelopes, and comparing with a threshold value. A metod for extracting a low-frequency signal envelope using the Hilbert transform is shown. A description of the cross-correlation analysis based on the Pearson correlation coefficient is given. The leakage channel simulation modeling, formation and measurement of the test signals was performed in the MatLab. The obtained results confirm the greater efficiency of using the envelope compared to the original signal, and demonstrate the speech signals advantage over harmonic signals as test signals for assessing the information leakage channel security.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΠΎΡΠ΅Π½ΠΊΠΈ Π·Π°ΡΠΈΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ°Π½Π°Π»Π° ΡΡΠ΅ΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π²Π·Π°ΠΈΠΌΠ½ΠΎ- ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π° Π² ΡΠ΅ΡΠ΅Π²ΠΎΠΌ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅ ΡΠ°ΡΡΠΎΡ. ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π° ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ Π΅Π³ΠΎ ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ, ΠΈΠ·Π»ΡΡΠ΅Π½ΠΈΠ΅ ΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ Π² ΠΊΠ°Π½Π°Π»Π΅ ΡΡΠ΅ΡΠΊΠΈ, Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠ΅ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΎΠ³ΠΈΠ±Π°ΡΡΠΈΠΌΠΈ, ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ Ρ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΡΠΌ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΌ. ΠΠΏΠΈΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π½ΠΈΠ·ΠΊΠΎΡΠ°ΡΡΠΎΡΠ½ΠΎΠΉ ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ ΡΠΈΠ³Π½Π°Π»Π° Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΠΈΠ»ΡΠ±Π΅ΡΡΠ°. ΠΡΠΈΠ²Π΅Π΄Π΅Π½ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π·Π°ΠΈΠΌΠ½ΠΎ-ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΠΈΡΡΠΎΠ½Π°. ΠΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ°Π½Π°Π»Π° ΡΡΠ΅ΡΠΊΠΈ, ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΡΡΠ΅Π΄Π΅ MatLab. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ Π±ΠΎΠ»ΡΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ³ΠΈΠ±Π°ΡΡΠ΅ΠΉ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠΈΠ³Π½Π°Π»ΠΎΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΠ΅ΡΠ΅Π΄ Π³Π°ΡΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠΈΠ³Π½Π°Π»Π°ΠΌΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π·Π°ΡΠΈΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ°Π½Π°Π»Π° ΡΡΠ΅ΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
ΠΡΠ΅Π½ΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π°Π½Π°Π»ΠΎΠ³ΠΎ-ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π²ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°
The paper presents a technique for estimating the information parameters of the quantization noise generated by the analog-to-digital conversion of the measuring signal. An experiment and algorithm descriptions are presented to confirm the correctness of the method for processing and extracting the information parameters of the signal and quantization noise, in which a connecting cable is used instead of a measuring antenna. The measurement results are shown in the form of graphs of the dependences of the quantization noise extraction time by accumulation on influencing factors and controlled parameters, which demonstrate the limiting values of the extraction of information parameters of the quantization noise of the analog-to-digital conversion of the measuring signal. Confirmation of the hypothesis of estimating the information parameters of the quantization noise is obtained.Β ΠΡΠΈΠ²Π΅Π΄Π΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠΎΡΠΌΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΎΠ³ΠΎ-ΡΠΈΡΡΠΎΠ²ΡΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΈ ΡΠΎΡΡΠ°Π² ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎ-Π½Π°ΡΡΡΠ½ΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΡΡΠΈ ΡΠΏΠΎΡΠΎΠ±Π° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠΈΠ³Π½Π°Π»Π° ΠΈ ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π²ΠΌΠ΅ΡΡΠΎ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ Π°Π½ΡΠ΅Π½Π½Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΠΊΠ°Π±Π΅Π»Ρ. ΠΠΎΠΊΠ°Π·Π°Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ Π² Π²ΠΈΠ΄Π΅ Π³ΡΠ°ΡΠΈΠΊΠΎΠ² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠ΅ΠΉ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΡΠ΅ΠΌ Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ ΠΎΡ Π²Π»ΠΈΡΡΡΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΡΠ΅ΠΌΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ², Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΡΡΠΈΠ΅ ΠΏΡΠ΅Π΄Π΅Π»ΡΠ½ΡΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π°Π½Π°Π»ΠΎΠ³ΠΎ-ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°. ΠΠΎΠ»ΡΡΠ΅Π½ΠΎ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΈΠ΅ Π³ΠΈΠΏΠΎΡΠ΅Π·Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΡΠΌΠ° ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ.
Data Composition and Representation for Cognitive Communication System Model Based on LTE
Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ΅ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ°ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠΏΠ΅ΠΊΡΡΠ° ΠΏΡΡΠ΅ΠΌ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄ΠΎΡΡΡΠΏΠ° ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΠΌ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ°Π΄ΠΈΠΎ Π² ΡΠ΅ΡΠΈ Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ ΡΠ΅ΡΠ²Π΅ΡΡΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ. Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΡΡΡΠΊΡΡΡΠ° ΠΈ ΡΠΎΡΠΌΠ°Ρ ΠΏΠ΅ΡΠ΅Π΄Π°Π²Π°Π΅ΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ ΡΠ΅ΡΠΈ LTE Π² ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΈ ΠΊ Π·Π°Π΄Π°ΡΠ΅ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ²ΡΠ·ΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠΎΠ»Ρ ΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ°ΡΡΡ ΡΠ°Π΄ΠΈΠΎΡΡΠ΅Π΄Ρ, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ ΡΡΡΡΠΊΡΡΡΠ° ΠΈ ΡΠΎΡΡΠ°Π² Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ Π΅Π΅ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ. ΠΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΡΡΠ΅Π΄Π΅ MatLab Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ°ΠΊΠ΅ΡΠ° LTE Waveform Generator, Π΄Π»Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈ Π·Π°ΠΏΠΈΡΠΈ Π΄Π°Π½Π½ΡΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ ΡΠΎΡΠΌΠ°Ρ HDF5. ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ.= The paper discusses to the problem of secondary use of the licensed frequency spectrum by providing access to users using cognitive radio technology in the fourth generation LTE wireless communication network. The structure and format of transmitted data in such a network are considered as applied to the problem of constructing a model of a cognitive communication system. The role and principles of the formation of a radio environment map are presented. Structure and composition of data for REM construction are proposed. Modeling of the formation and presentation of data in the MatLab environment using the LTE Waveform Generator package has been performed. HDF5 is used for data recording. The simulation results are presented