• ETRI Journal
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• v.45 no.5
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• pp.811-821
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• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5271-5289
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• 2016

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.240-240
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• 2000

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1673-1678
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• 2009

Multicast can transmit data packet to multiple terminals by using only one transmission and enhances the system performance. However, the multicast transmission rate is fixed and the system performance is not optimized. In this paper, we propose an adaptive multicast rate control method. In the proposed method, orthogonal subcarrier is assigned to each terminal. Each terminal informs the channel status using the allocated subcarrier. Transmitter selects the optimal rate using the feedback information. With the proposed adaptive rate control method, the system performance is enhanced compared with the legacy multicast method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2998-3017
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• 2018

Massive (large-scale) MIMO (multiple-input multiple-output) is one of the key technologies in next-generation wireless communication systems. This paper proposes a high-performance low-complexity turbo receiver for SC-FDMA (single-carrier frequency-division multiple access) based MMIMO (massive MIMO) systems. Because SC-FDMA technology has the desirable characteristics of OFDMA (orthogonal frequency division multiple access) and the low PAPR (peak-to-average power ratio) of SC transmission schemes, the 3GPP LTE (long-term evolution) has adopted it as the uplink transmission to meet the demand high data rate and low error rate performance. The complexity of computing will be increased greatly in base station with massive MIMO (MMIMO) system. In this paper, a low-complexity adaptive turbo equalization receiver based on normalized minimal symbol-error-rate for MMIMO SC-FDMA system is proposed. The proposed receiver is with low complexity than that of the conventional turbo MMSE (minimum mean square error) equalizer and is also with better bit error rate (BER) performance than that of the conventional adaptive turbo MMSE equalizer. Simulation results confirm the effectiveness of the proposed scheme.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.975-983
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• 1999

최근 네트워크의 속도가 빨라지고 멀티미디어 데이터를 다루기 위한 기술들이 개발됨에 따라 많은 멀티미디어 응용 프로그램들이 인터넷에 등장하고 있다. 그러나 이들 응용프로그램들은 수신자에게 전송되는 영상.음성의 품질이 낮기 때문에 기대만큼 빠르게 확산되지 못하고 있다. 영상.음성의 품질이 낮은 이유는 현재 인터넷이 실시간 응용프로그램이 요구하는 만큼 빠르고 신뢰성 있게 데이터를 전송할 수 없기 때문이다. 현재 인터넷의 내부구조를 바꾸지 않고 품질을 높이기 위해 많은 연구들이 진행되고 있는데 그 중 하나는 동적으로 변화하는 인터넷의 상태에 맞게 멀티캐스트 트래픽의 전송율을 조절하는 종단간의 흐름제어이다. 본 논문은 기존의 흐름제어 기법인 IVS와 RLM의 성능을 개선시키기 위한 두 가지 흐름제어 기법을 소개한다. IVS는 송신자가 주기적으로 측정된 네트워크 상태에 따라 전송율을 일정하게 조절한다. 송신자가 하나의 데이타 스트림을 생성하는 IVS와는 달리 RLM에서는 송신자가 계층적 코딩에 의하여 생성된 여러개의 데이타 스트림을 전송하고 각 수신자는 자신의 네트워크 상태에 맞게 데이타 스트림을 선택하는 기법이다. 그러나 IVS는 송신자가 전송율을 일정하게 증가시키고, RLM은 각자의 네트워크 상태를 고려하지 않고 임의의 시간에 하나 이상의 데이타 스트림을 받기 때문에 성능을 저하시킬 수 있다. 본 논문에서는 TCP-like IVS와 Adaptive RLM이라는 두 가지 새로운 기법을 소개한다. TCP-like IVS는 송신자가 전송율을 동적으로 결정하고, Adaptive RLM은 하나 이상의 데이타 스트림을 받기 위해 적당한 시간을 선택할 수 있다. 본 논문에서는 시뮬레이션을 통해 여러 가지 네트워크 구조에서 두 가지 방식이 기존의 방식에 비하여 더욱 높은 대역폭 이용율과 10~20% 정도 적은 패킷손실율을 이룬다는 것을 보여준다.Abstract Nowadays, many multimedia applications for the Internet are introduced as the network gets faster and many techniques manipulating multimedia data are developed. These multimedia applications, however, do not spread widely and are not fast as expected at their introduction time due to the poor quality of image and voice delivered at receivers. The poor quality is mainly attributed to that the current Internet can not carry data as fast and reliably as the real-time applications require. To improve the quality without modifying the internal structure of the current Internet, many researches are conducted. One of them is an end-to-end flow control of multicast traffic adapting the sending rate to the dynamically varying Internet state. This paper proposes two flow-control techniques which can improve the performance of the two conventional techniques; IVS and RLM. IVS statically adjusts the sending rate based on the network state periodically estimated. Differently from IVS in which a sender produces one single data stream, in RLM a sender transmits several data streams generated by the layered coding scheme and each receiver selects some data streams based on its own network state. The more data streams a receiver receives, the better quality of image or voice the receiver can produce. The two techniques, however, can degrade the performance since IVS increases its sending rate statically and RLM accepts one more data stream at arbitrary time regardless of the network state respectively. We introduce two new techniques called TCP-like IVS and Adaptive RLM; TCP-like IVS can determine the sending rate dynamically and Adaptive RLM can select the right time to add one more data stream. Our simulation experiments show that two techniques can achieve better utilization and less packet loss by 10-20% over various network topologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.1946-1958
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• 2011

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.53-58
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• 2022

Smoothing is a transmission plan that converts video data stored at a variable bit rate into a constant bit rate. In the study of [6-7], when a data rate increase is required, the frame with the smallest increase is set as the start frame of the next transmission rate section, when a data tate decrease is required. the frame with the largest decrease is set as the start frame of the next transmission rate section, And the smoothing algorithm was proposed and performance was evaluated in an environment where network traffic is not considered. In this paper, the smoothing algorithm of [6-7] evaluates the adaptive CBA algorithm and performance with minimum frame rate, average frame rate, and frame rate variation from 512KB to 32MB with E.T 90 video data in an environment that considers network traffic. As a result of comparison, the smoothing algorithm of [6-7] showed superiority in the comparison of the minimum refresh rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2070-2086
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• 2014

As a kind of personal lifelog data, activity data have been considered as one of the most compelling information to understand the user's habits and to calibrate diagnoses. In this paper, we proposed a robust algorithm to sampling rates for human activity recognition, which identifies a user's activity using accelerations from a triaxial accelerometer in a smartphone. Although a high sampling rate is required for high accuracy, it is not desirable for actual smartphone usage, battery consumption, or storage occupancy. Activity recognitions with well-known algorithms, including MLP, C4.5, or SVM, suffer from a loss of accuracy when a sampling rate of accelerometers decreases. Thus, we start from particle swarm optimization (PSO), which has relatively better tolerance to declines in sampling rates, and we propose PSO with an adaptive boundary correction (ABC) approach. PSO with ABC is tolerant of various sampling rate in that it identifies all data by adjusting the classification boundaries of each activity. The experimental results show that PSO with ABC has better tolerance to changes of sampling rates of an accelerometer than PSO without ABC and other methods. In particular, PSO with ABC is 6%, 25%, and 35% better than PSO without ABC for sitting, standing, and walking, respectively, at a sampling period of 32 seconds. PSO with ABC is the only algorithm that guarantees at least 80% accuracy for every activity at a sampling period of smaller than or equal to 8 seconds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.22-33
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• 2005

In this paper, an efficient signal interference control technique to improve the high convergence speed of LMS algorithms is introduced in the adaptive transversal filter of DS/SS. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is analyzed to prove theoretically the improvement of high convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Also, an increase in the stop-size parameter ${\mu}$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling which is downed the rate of convergence of the adaptive equalizer. Increasing the steady-state value of the average squared error, proposed algorithm also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.