Earticle

차량과 같이 넓은 범위를 이동하는 환경에서 무선 인터넷 서비스를 사용할 때, 불필요하게 발생하는 핸드오버는 서비스 품질 저하의 주된 요인이다. 본 논문에서는 차량이 일정한 이동패턴을 갖고 있을 때, 핸드오버 빈도를 줄일 수 있는 방안을 제시한다. 차량의 이동 패턴을 Discrete-Time Markov Chain (DTMC)으로 모델링하고, AdaBoost 기법을 이용하여, 각 셀 내의 체류시간과 평균 신호의 세기를 저장하고 분석하여 핸드오버 시, 적합한 목적지 셀을 정하도록 한다. 제안한 방안의 검증을 위해, 서울 시내버스 노선들을 기반으로 성능 평가를 수행하였으며, 결과로 기존의 핸드오버 기법보다 핸드오버의 빈도를 줄이며, 평균 처리율 (throughput)은 비슷한 레벨로 유지할 수 있었다.

Redundant handovers cause degraded quality of service to passengers in vehicle. This paper proposes a handover scheme suitable for users traveling in vehicles, which enables continuous learning of the handover process using a discrete-time Markov chain (DTMC). Through AdaBoost machine learning algorithm, the proposed handover scheme avoids unnecessary handover trials when a short dwell time in a target cell is expected or when the target cell is an intermediate cell through which the vehicle quickly passes. Simulation results show that the proposed scheme reduces the number of handover occurrences and maintains adequate throughput.

A novel feature representation method based on AdaBoost algorithm is put forward for action recognition in this paper. The method can not only adequately describe action in complex scenarios, but also select the most discriminative sample subset from a large amount of raw features of training data. So it can realize a double result, that is, reduce the recognition computational complexity and achieve a good recognition accuracy. The pyramid histogram of oriented gradient feature (PHOG) descriptor is utilized to represent raw feature data. In order to select most discriminative samples subset, AdaBoost algorithm is used to extract the raw feature data. The nearest neighbor classifier algorithm is utilized to test the proposed method on the UCF Sports database. Experiment results show that the method not only achieve the better recognition rate but also greatly improve the speed of recognition.

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar features as feature vectors, and downscaling with integral image. The result shows that we can get nearly 6x from the standard code to with our CPU implementation to achieve a near real-time performance and ensure better classification results in misjudgment.

Robust detection of humans in image sequences is important for many applications. However, if humans are adjacent to each other, it is much more difficult to accurately detect them. In this paper, we propose a method to automatically detect multiple humans using motion information and Adaboost algorithm from a single camera on a mobile or stationary system. In case of mobile system, the ego-motion of the camera is compensated by the corresponding feature sets. The region of interest that moving objects are likely to exist is searched by the projection approach using a difference image between two consecutive images that an ego-motion is compensated. Human detector is learned by boosting a number of weak classifiers which are based on Harr-like features. The proposed approach has been tested to a number of image sequences, and it was shown to detect multiple humans very well.

According to the limitations of a single measurement algorithm in the current 3D models’ viewpoint extraction, this essay puts forward a viewpoint extraction algorithm based on AdaBoost iterative algorithm, which can make the features adaptive automatically. It, firstly, extracts 3D models’ feature descriptor and feature vector in the model library and adopts AdaBoost iterative algorithm to establish rules about classification and matching from geometric features and various viewpoint extraction algorithm; then, it constructs decision classifier in order to extract optimal viewpoint. In query process, the model obtains viewpoint extraction algorithm which can suit its geometric feature through decision classifier and then gets its best view by calculation. The experimental result shows this algorithm extraction effect is superior to the one by a single measurement algorithm.

통계청에 따르면 심혈관 등의 성인병 질환으로 연 600~800명이 사망하는 것으로 나타나고, 고혈압, 동맥경화증, 심장병, 뇌졸중 등은 혈액의 흐름에 장애가 생겨 발생하는 심혈관계질환으로 오늘날 성인병의 주종을 이루고 있는 사망률이 높은 질병으로 구분된다. 또한 사망한 심혈관질환자 중 올바른 응급처치를 했더라면 생존했을 환자가 약 40%를 차지하고 있어 응급상황 발생 시 신속한 대응이 요구된다. 따라서 본 논문에서는 AdaBoost알고리즘의 weak classifier를 결합하여 strong classifier를 생성하는 방법을 통하여 효과적인 분석으로 심전도를 측정할 수 있도록 하고, 심혈관 질환자에게 발생한 응급상황을 빠른 시간 내에 관리 데스크에 전달할 수 있는 시스템을 제안하였다. 이에 따라 심전도 센서를 기반으로 측정한 데이터를 ZigBee통신으로 단말기에 전송하고 응급 상황을 판정하여 관리 데스크에 긴급경보와 모니터링을 제공함으로써 신속한 의료서비스 제공이 가능하도록 하였다.

Diseases such as cardiovascular illnesses, according to the National Statistical Office opened reveals that 600-800 people were killed, blood pressure, arteriosclerosis, heart disease, stroke, etc. will be a flow of blood disorders that occur in cardiovascular illnesses today are fulfilling the Master / Slave samangryulin disease appears high. Died of cardiovascular disease also told them the correct first aid survival when patients are accounted for approximately 40% of emergency rapid response is required. Therefore, this paper, the weak classifier in the AdaBoost algorithm to generate a strong classifier by combining effects throughout the analysis to measure the ECG, and cardiovascular disease that occurred to you as soon as the emergency management system that can deliver on the proposed Desk was. The electrocardiogram data measured by the ZigBee-based sensors, communication devices and emergency transport for emergency alarms in the determination and monitoring of the management desk by providing health services to enable the delivery was fast.