Earticle

Consider a large-scale distributed system in which each computing device is observing triggers from an external source. Distributed Trigger Counting (DTC) algorithm is used to detect the state of the system when the aggregated number of the observed triggers reaches a predefined value. In this paper, we propose a simple and efficient DTC algorithm: Cascading Thresholds (CT). We mathematically show that CT is an optimal DTC algorithm in terms of the total number of exchanged messages among the devices (message complexity). For the maximum number of received messages per device (MaxRcv), CT is sub-optimal. The average message complexity of CT is , and MaxRcv of it is , where is the number of triggers to be detected, is the number of devices, and is the degree of a node in the tree-like structure. Compared to the previous optimal algorithm (TreeFill), CT is much simpler: in our implementation the code size is about 2.5 times smaller. Also, unlike TreeFill CT does not require complicated mechanisms including distributed locking. Experimental results show that CT has a lower message complexity and MaxRcv compared to the previous work (CoinRand and RingRand). Furthermore, CT and TreeFill show a similar performance. From its simplicity, CT is more practical than previous work including TreeFill, CoinRand and RingRand.

Owing to the increasing social concerns caused by high traffic density in developing countries, governments should incorporate intelligent traffic systems to regulate traffic. Vehicle detection, tracking, and counting play important roles in vision-based intelligent traffic systems. Robustness to errors (i.e., missed and doubled counts) is essential in traffic systems. Although the existing Marker algorithm can prevent missed counts, it provides a doubled count when two or more binary objects come from a vehicle. This study applies and analyzes the Catcher algorithm to three highway lanes to prove whether it can prevent the double count occurrence. The results showed that compared to the Marker algorithm, the Catcher algorithm demonstrates better accuracy for the tested cases. Its initial accuracy reached 94.26% and increased further after adjusting the size of the region of interest. In conclusion, the Catcher algorithm can prevent a double count by rounding down when two or more binary objects originate from a vehicle.

Regression testing remains one of the most laborious and costly software testing activities in software maintenance. Its laboriousness and costing increases with increasing number of test suits: need to changed re-testing strategy. The strategy should identify and eliminate redundant test cases in order to minimize the test cases from test suites. The aim of regression testing is to reduce the retesting effort and achieve the adequate testing coverage. An important research problem, in this context, is the new developed algorithm is needed to reduce the retesting effort, cost and time without disturbance of thoroughness. The traditional optimal page replacement algorithm is used for managing the virtual memory. Our approach has been showing that the novel optimal page replacement algorithm reducing the redundant test cases during retesting of modified object oriented program. Our testing approach is on unit level. The logic based transitive relationship model achieves the adequate testing coverage to the modified object oriented program. Hybridization of clock page replacement-counting based algorithm is used for making the logic based transition relationship model. An Illustrative example has been presented to establish the effectiveness of our methodology coupled with logic based transitive relationship model.

Various sensors have been recently integrated in a smart-phone and step detection method based on the acceleration sensor of a smart-phone has been introduced for indoor positioning scheme. Many researchers have put their interests on the study of step detection algorithm based on the acceleration sensor. However, the estimation performances of these methods are not good enough or just suitable for the situation when the position of the smart-phone is fixed in hand because the performance is significantly degraded in other cases. In this paper, a novel step counting algorithm based on the acceleration and the gravity sensors is proposed to enhance the estimation performance regardless of the position of a smart-phone and the motions of a pedestrian likewise walking or running. The effectiveness of the proposed scheme is demonstrated with experiments and the performance of the proposed scheme is compared with those of the conventional schemes. According to the results, the performance of the proposed scheme is enhanced compared to the conventional schemes no matter what the pedestrian is walking or running although the phone is in different places like trouser pocket, shirt pocket and hands.

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.

전류 적산법(Coulomb counting, ampere counting)을 이용한 배터리 SOC(State-of-Charge) 추정 방법은 상용화된 IC를 사용할 수 있기에 구현이 간단하고 SOC 정의를 통해 배터리 사용 가능한 시간을 쉽게 예측할 수도 있다. 하지만 초기 SOC 오류와 누적되는 전류 정보의 오차로 인해 추정이 실패하는 단점이 존재하기 때문에 이를 해결해주는 알고리즘이 필요하다. 본 논문에서는 전류 적산법 기반의 배터리 SOC 추정 회로에 확장 칼만 필터(EKF, Extended Kalman Filter)를 접목하여 전류 적산법을 이용하였을 때 나타날 수 있는 오차 누적을 줄이는 알고리즘을 제안한다. 또한 실험을 통해 제안된 배터리 SOC 추정 회로의 성능을 확인해본다.

본 논문은 OCV 추정기법을 이용한 전류적산법의 SOC Reset 알고리즘을 제안한다. 제안한 알고리즘은 배터리 상황에 따라 OCV 추정을 위한 휴지시간을 달리 설정한다. 이에 따라 짧은 휴지 시에도 SOC Reset Point를 늘려 전류적산법의 오차를 Reset함으로써 SOC 추정 능력을 향상한다. Li-ion 27 Ah/99.9 Wh 배터리의 충 방전 실험을 통해서 OCV 판별 시간을 도출하여 알고리즘을 구현한다. 전기자동차의 주행 패턴을 모사하여 기존의 전류적산법과 비교 실험을 통해 제안한 알고리즘을 검증한다.

분산 트리거 계수 문제는 외부로부터 트리거를 수신하는 n개의 노드로 구성된 분산 시스템에서 수신한 전체 트리거 수의 합이 w에 이르렀을 때 이를 사용자에게 알려주는 문제로 무선 센서 네트워크를 포함한 다양한 분산 시스템 환경에서 모니터링과 전역 스냅샷을 위해 사용된다. 이 논문에서는 분산 트리거 계수 문제를 위한 간단하고 효율적인 알고리즘을 제안한다. 제안 알고리즘은 차수가 $\sqrt{n}$이고 높이가 2인 트리 구조를 기반으로 동작하며, 남은 트리거 수에 따라 세 단계의 서로 다른 방식으로 동작한다. 제안 알고리즘에서 수신한 전체 트리거 수의 합이 w에 이르렀을 때 이를 사용자에게 알려주지 않을 확률이 0임을 증명하였고, 실험을 통해 제안 알고리즘이 CoinRand에 비해 더 작은 메시지 복잡도를 가지며 노드 수가 많지 않을 때는 MaxRcv 또한 더 작음을 보여주었다.

The distributed trigger counting problem is to notify the user when the total number of triggers received from a distributed system consisting of n nodes reaches a predefined value w. The distributed trigger counting problem is used for monitoring and global snapshots in various distributed systems. In this paper, we propose a simple and efficient algorithm for the distributed trigger counting problem. The proposed algorithm operates based on a tree structure of degree $\sqrt{n}$ and height 2. The proposed algorithm operates in three different phases based on the remaining number of triggers. Experimental results show that the proposed algorithm has a smaller message complexity than CoinRand, and MaxRcv also performed better when the number of nodes is not large.

In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.