With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to o...With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.展开更多
With the development of genome sequencing for many organisms, more and moreraw sequences need to be annotated. Gene prediction by computational methods for finding thelocation of protein coding regions is one of the e...With the development of genome sequencing for many organisms, more and moreraw sequences need to be annotated. Gene prediction by computational methods for finding thelocation of protein coding regions is one of the essential issues in bioinformatics. Two classes ofmethods are generally adopted: similarity based searches and ab initio prediction. Here, we reviewthe development of gene prediction methods, summarize the measures for evaluating predictor quality,highlight open problems in this area, and discuss future research directions.展开更多
基金supported by the National Natural Science Foundation of China(61571162)the Major National Science and Technology Project(2014ZX03004003-005)
文摘With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.
文摘With the development of genome sequencing for many organisms, more and moreraw sequences need to be annotated. Gene prediction by computational methods for finding thelocation of protein coding regions is one of the essential issues in bioinformatics. Two classes ofmethods are generally adopted: similarity based searches and ab initio prediction. Here, we reviewthe development of gene prediction methods, summarize the measures for evaluating predictor quality,highlight open problems in this area, and discuss future research directions.
