• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.948-955
• /
• 2019

As interests in 360-degree VR (Virtual Reality) video services enormously grow, compression and streaming technologies for VR video data have been rapidly developed. Quality Emphasized Region (QER) based streaming scheme has been developed as a kind of viewport-adaptive 360-degree video streaming technology for maintaining immersive experience and reducing bandwidth waste. For selecting a QER corresponding to the user's gaze coordinate, QER-based streaming scheme requires the calculation of Quality Emphasis Center (QEC) distance and signaling message delivery for requesting QER switching. QEC distance calculations require high computational complexity because of repeated calculations as many times as the number of QERs. Furthermore, the signaling message interval results in a trade-off relationship between efficient bandwidth usage and flexible QER switching. In this paper, we propose an improved QER selection algorithm based on MMT protocol to solve this problem. The proposed algorithm could achieve computational complexity reduction by using preprocessed QER_ID_MAP. Also, flexible QER switching could be achieved, as well as efficient bandwidth utilization by an adaptive adjustment of the signaling interval.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.1
• /
• pp.77-83
• /
• 2009

This paper proposes a new noise-biased compensation of minimum statistics(MS) method using a nonlinear function and a priori speech absence probability(SAP) for speech enhancement in non-stationary noisy environments. The minimum statistics(MS) method is well known technique for noise power estimation in non-stationary noisy environments. It tends to bias the noise estimate below that of true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori speech absence probability (SAP) for biased compensation. Specifically. we apply the adaptive parameter according to the a posteriori SNR. In addition, when the a priori SAP equals unity, the adaptive biased compensation factor separately increases ${\delta}_{max}$ each frequency bin, and vice versa. We evaluate the estimation of noise power capability in highly non-stationary and various noise environments, the improvement in the segmental signal-to-noise ratio (SNR), and the Itakura-Saito Distortion Measure (ISDM) integrated into a spectral subtraction (SS). The results shows that our proposed method is superior to the conventional MS approach.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.2
• /
• pp.239-247
• /
• 2018

Adaptive Beamforming (ABF) algorithm, which is a typical jammer suppression algorithm, guarantees the performance on the assumption that the jamming characteristics of the TDS (Training Data Sample) are stationary, which are obtained immediately before and after transmitting the pulse signal. Therefore, effective jammer suppression can not be expected when the jamming characteristics are non-stationary. In this paper, we propose a new jammer suppression algorithm, of which power spectrum fluctuates fast. In this case, we assume that the location of the jammer station is fixed during the processing time. By applying the MPM (Matrix Pencil Method) to the jamming signal in TDS, we can estimate jammer parameters such as power and incident angle, of which the power will vary fast in time or range bins after TDS. Though we assume that the jammer station is fixed, the estimated jammer's incident angle has an error due to the noise, which degrades the performance of the jammer suppression as the jammer power increases fast. Therefore, the jammer's incident angle should be re-estimated at each range bin after TDS. By using the re-estimated jammer's incident angle, we can construct new covariance matrix under the non-stationary jamming environment. Then, the optimum weight for the jammer suppression is obtained by inversing matrix estimation method based on the matrix projection with the estimated jammer parameters as variables. To verify the performance of the proposed algorithm, the SINR (signal-to-interference plus noise ratio) loss of the proposed algorithm is compared with that of the conventional ABF algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.1B
• /
• pp.39-49
• /
• 2012

With group mobility, minimizing routing overhead among intra-group nodes can be achieved by making longer the routing update period, which may lead to degradation of inter-group's routing performance and consequently the inter-group packet delivery success ratio will be decreased. To reduce the inter-group routing overhead without lowering the inter-group packet delivery success ratio, a mobility-adaptive routing update(MARU) scheme is proposed in this paper. Main idea of the proposed scheme is group-based mobility prediction and updating routing paths by selecting intermediate common relay groups.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.7 s.361
• /
• pp.35-40
• /
• 2007

As the mobile communication service is widely used and the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem that the output of the transmit antenna is partially fed back to the receive antenna, which results in feedback interference. In this paper, we propose a new varable step-size LMS algorithm which utilizes correlation between reference and error signals to adjust the step sizes, for cancelling the feedback interference signals in the WCDMA repeater under time-varying multi-path channels. The proposed algorithm was evaluated through computer simualation by being applied to the feedback canceling filter of the WCDMA repeater. The simulation results demonstrated that the proposed one is superior to the conventional ones in terms of the cancelation perormance.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.12
• /
• pp.509-516
• /
• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.5
• /
• pp.155-164
• /
• 2015

This paper describes a design and implementation of CABAC decoder, which would handle HEVC syntax elements in adaptively pipelined-parallel computation manner. Even though CABAC offers the high compression rate, it is limited in decoding performance due to context-based sequential computation, and strong data dependency between context models, as well as decoding procedure bin by bin. In order to enhance the decoding computation of HEVC CABAC, the flag-type syntax elements are adaptively pipelined by precomputing consecutive flag-type ones; and multi-bin syntax elements are decoded by processing bins in parallel up to three. Further, in order to accelerate Binary Arithmetic Decoder by reducing the critical path delay, the update and renormalization of context modeling are precomputed parallel for the cases of LPS as well as MPS, and then the context modeling renewal is selected by the precedent decoding result. It is simulated that the new HEVC CABAC architecture could achieve the max. performance of 1.01 bins/cycle, which is two times faster with respect to the conventional approach. In ASIC design with 65nm library, the CABAC architecture would handle 224 Mbins/sec, which could decode QFHD HEVC video data in real time.

• Steel and Composite Structures
• /
• v.34 no.5
• /
• pp.743-767
• /
• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.9
• /
• pp.361-368
• /
• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.2B
• /
• pp.120-129
• /
• 2012

Wireless mesh network (WMN) has recently emerged as a promising technology for tactical operation. If a platoon is organized with MPs, this system is suitable for tactical coverage is given for battle field where there is a shortage of wired infrastructure. However, MPs of typical WMN are generally fixed. This condition does not apply to diverse tactical scenarios. In this paper, it is considered that MPs have group mobility for flexible tactical networks. We propose cluster based multi-channel MAC scheme for mobilie WMN with single antenna condition. We have reduced the collision problems and message storming problems occur by mobility, so the reliability of WMN has been improved. Consequently, reliable communication is guaranteed by our framework in mobile WMN.