• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1145-1157
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• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.879-885
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• 2004

This paper describes an adaptive signal processing of digital receiver with digital down convertor(DDC). DDC is composed of numerically controlled oscillator(NCO) and digital low pass filler and the received signal is processed by numerical algorithm. The simulation results of digital receiver using the passband sampling technique are presented and we confirmed that the received low IF signal is converted to zero IF by numerically processed DDC. Direction of arrival(DOA) estimation technique using multiple signal classification(MUSIC) algorithm with high resolution is also discussed. We knew that an accurate resolution of DOA depends on the input sampling numbers and antenna element numbers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2838-2858
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• 2019

Traditional channel models for vehicle-to-vehicle (V2V) communication usually assume fixed velocity in static scattering environment. In the realistic scenarios, however, time-variant velocity for V2V results in non-stationary statistical properties of wireless channels. Dynamic scatterers with random velocities and directions have been always utilized to depict the non-stationary statistical properties of the channel. In this paper, a non-stationary geometry-based cooperative scattering channel model is proposed for multiple-input multiple-output (MIMO) V2V communication systems, where a birth-death process is used to capture the appearance and disappearance dynamic properties of moving scatterers that reflect the time-variant time correlation and Doppler spectrum characteristics. Moreover, our model has more straight and concise to study the impact of the vehicular traffic density on channel characteristics and thus avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions. The numerical results validate our analysis and demonstrate that setting important parameters of our model can appropriately build up more purposeful measurement campaigns in the future.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• ETRI Journal
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• v.26 no.6
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• pp.565-574
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• 2004

Differential unitary space-time modulation (DUSTM) has emerged as a promising technique to obtain spatial diversity without intractable channel estimation. This paper presents a study of the application of DUSTM on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with frequency-selective fading channels. From the view of a correlation analysis between subcarriers of OFDM, we obtain the maximum achievable diversity of DUSTM on MIMO-OFDM systems. Moreover, an efficient implementation strategy based on subcarrier reconstruction is proposed, which transmits all the signals of one signal matrix in one OFDM transmission and performs differential processing between two adjacent OFDM blocks. The proposed method is capable of obtaining both spatial and multipath diversity while reducing the effect of time variation of channels to a minimum. The performance improvement is confirmed by simulation results.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.877-880
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• 1991

In this paper, the architecture of a dataflow logic solving processor for programmable logic controller is proposed. As the proposed DFLSP(dataflow logic solving processor) is designed based on the dataflow architecture, it has inherently concurrent processing and data synchronization capabilities. And also, it has dynamic load balancing capabilites which increases the utilization of the whole system that can he hardly implemented in other multiprocessor system. The re-matching unit gets rid of unnecessary matching cycles in LSU, which increases the performance of LSU and allows the multiple input multiple output operations.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.39-44
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• 2010

A method is presented of dynamic determination of mode transition probability for IMM in order to improve the accuracy performance of maneuvering target tracking for air traffic control surveillance processing system under multiple radar environment. It is shown that dynamic determination of mode transition probability based on the time intervals between the data input from multiple radars gives the optimized performance in terms of position estimation accuracy.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.83-87
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• 2014

This paper introduces a refined opportunistic interference alignment (OIA) technique that uses minimum mean square error (MMSE) detection at the receivers in multiple-input multiple-output multi-cell uplink networks. In the OIA scheme under consideration, each user performs the optimal transmit beamforming and power control to minimize the level of interference generated to the other-cell base stations, as in the conventional energy-efficient OIA. The result showed that owing to the enhanced receiver structure, the OIA scheme shows much higher sum-rates than those of the conventional OIA with zero-forcing detection for all signal-to-noise ratio regions.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.240-245
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• 2010

Jacket matrices, motivated by the complex Hadamard matrix, have played important roles in signal processing, communications, image compression, cryptography, etc. In this paper, we suggest a novel approach to design a simple class of space-time block codes (STBCs) to reduce its peak-to-average power ratio. The proposed code provides coding gain due to the characteristics of the complex Hadamard matrix, which is a special case of Jacket matrices. Also, it can achieve full rate and full diversity with the simple decoding. Simulations show the good performance of the proposed codes in terms of symbol error rate. For generality, a kind of quasi-orthogonal STBC may be similarly designed with the improved performance.