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Han, Il
- A Method of Multi-Channel Data Reception and Processing Noise for Short Distance Detection Doppler Radar
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Authors
Affiliations
1 Institute of Information Science, Kim Il Sung University, D.P.R. of Korea, KP
1 Institute of Information Science, Kim Il Sung University, D.P.R. of Korea, KP
Source
ICTACT Journal on Microelectronics, Vol 4, No 2 (2018), Pagination: 608-612Abstract
In short distance detection Doppler radar signal processing, the communication and processing problems of various measurement and control signals such as mass data which are high speed A/D converted intermediate frequency signals of transmission and reception terminals and azimuth angle, altitude are suggested. In this paper we suggest one method for time sharing data communication and noise processing by determining threshold by using the transmission line of limited bits in connection of analog circuits consisting of electromagnetic transmission and reception antenna and various control, measuring circuits and FPGA+DSP type digital signal processing circuits and showed experiment results.Keywords
Doppler, DSP+FPGA, Digital Signal Processing System.
References
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- Neighborhood Loss for Age Estimation from Face Image Using Convolutional Neural Networks
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Authors
Affiliations
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2770-2774Abstract
Convolutional Neural Network (CNN) is widely used in estimating age from face image. In many CNN applications such as image classification, face recognition and other computer vision scopes, the cross-entropy loss is used as a supervision signal to train CNN model. However, the cross-entropy loss only enhances the separability of classes and does not consider their correlation in age estimation task. In this paper we propose a novel loss function called neighborhood loss which regards the correlation between classes in age estimation by modifying standard cross entropy loss. To evaluate the effectiveness of the proposed neighborhood loss, we present CNN architecture based on the residual units. Through some experiments, we show that neighborhood loss provides superior performance compared to prior works in age estimation.Keywords
Age Estimation, Neighborhood Loss, Convolutional Neural Network.References
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- A Gated Recurrent Unit Based Robust Voice Activity Detector
Abstract Views :57 |
PDF Views:3
Authors
Affiliations
1 Institute of Information Technology, Hightech Research & Development Center Kim Il Sung University, Pyongyang, KP
1 Institute of Information Technology, Hightech Research & Development Center Kim Il Sung University, Pyongyang, KP
Source
International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5831-5836Abstract
Voice activity detection (VAD), which identifies speech and non-speech durations in speech signals, is a challenging task under noisy environment for various speech applications. In this paper, we propose a Gated Recurrent Unit (GRU) based VAD using MFCCs augmented delta and delta-delta features under the low signal-to-noise ratios (SNRs) environments to overcome the shortages of the traditional VAD models. We compare the proposed method with the traditional methods by using speech signals smeared with 10 types of noise at low SNRs. Experimental results reveal that the proposed method based on GRU is superior to traditional method under all the considered noisy environments, indicating that the network based on GRU improve the performance of speech detection.Keywords
voice activity detection, deep neural network, recurrent neural network, gated recurrent unit.Full Text
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