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5th IEEE Global Conference on Consumer Electronics

作者： Takahashi, Chiaki Kondo, Kazuhiro Yamagata Univ Grad Sch Sci & Engn Yamagata Japan

ISBN: (纸本)9781509023332

We studied the feasibility of the use of iBeacons for position detection. We compared the precision achieved by two well-known algorithms, i.e. nearest neighbor and k-nearest beacon. The nearest neighbor algorithm showed lower average error distance, by 1.0 m, than the k-nearest beacon algorithm. We also found that both indoor position methods are not influenced by the difference in the measured rooms. This accuracy should be enough for its application to navigation system using augmented audio reality, to be implemented in the future.

关键词： iBeacon indoor positioning method nearest neighbor algorithm Bluetooth LE

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nearest neighbor algorithm for Positive and Unlabeled Learning with Uncertainty

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计算机科学与探索 2010年第9期4卷 769-779页

作者： Pan Shirui Zhang Yang Li Xue Wang Yong College of Information Engineering Northwest A&F University Shaanxi Yangling 712100 College of Information Engineering Northwest A&F University State Key Laboratory for Novel Software Technology Shaanxi Yangling 712100 School of Information Technology and Electrical Engineering University of Queensland Australia Brisbane 4072 School of Computer Northwestern Polytechnical University Xi'an 710072

This paper studies the problem of uncertain data classification under positive and unlabeled(PU)learning scenario. It proposes a novel algorithm,NNPU(nearest neighbor algorithm for positive and unlabeled learning),to handle this problem with two varieties,NNPUa and NNPUu. Experimental results on benchmark UCI datasets show that NNPUu, which considers the whole uncertain information on the datasets, has a better ability to classify unseen examples than NNPUa that considers the average value of uncertainty only. Furthermore,NNPU outperforms some existing algorithms such as NN-d,OCC(one-class classifier)and aPUNB in handling precise data

关键词： uncertain data positive and unlabeled learning nearest neighbor algorithm

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Incremental Learning Based on the nearest neighbor Classifier 12

Incremental Learning Based on the Nearest Neighbor Classifie...

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Liu, Sunfu Ye, Qing Liu, Xiang Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781467376822

In many practical applications, it is difficult to obtain the complete sample set and the categories may be variable with time. This paper introduces an online incremental learning algorithm based on the nearest neighbor algorithm to increase model or category during the process of recognition. Calculating the matching degree between new input sample and model samples, the algorithm finds the best and second best matching degrees and compares them with the threshold. The comparative results decide whether it increases samples' amount or adds a new category to realize incremental learning. The algorithm is applied to segment data set and experiment of vehicle type recognition. The experiments prove the algorithm efficient. Also, more experiments are conducted to analyze and verify how standard sample amount and matching degree threshold affect the final results.

关键词： Incremental Learning nearest neighbor algorithm Matching Degree

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Self-Organizing Map Neural Network-Based Depth-of-Interaction Determination for Continuous Crystal PET Detectors

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2015年第3期62卷 766-772页

作者： Wang, Yonggang Wang, Liwei Li, Deng Cheng, Xinyi Xiao, Yong Univ Sci & Technol China Dept Modern Phys Hefei 230026 Anhui Peoples R China

For continuous crystal-based PET detectors, not only the two-dimensional (2D) plane coordinate of the interaction point, but also the depth-of-interaction (DOI) of the event could be precisely estimated by the single-end readout of the scintillation light. In this paper, we propose a practical method for DOI determination for continuous crystal PET detectors. By self-organizing map (SOM) neural network with unsupervised learning, the perpendicularly irradiated reference events in each reference position are classified into a certain number of groups, which are simultaneously used for the plane coordinate estimation and the DOI decoding. The reference events measured in an oblique irradiation are used to generate the initial weights of the SOM neurons and to calibrate the DOI decoding. Applying the new method to our experimental data, the SOM-based DOI estimation could achieve an average resolution of 2.56 mm over the whole thickness (0-10 mm) of the crystal. The DOI effect is also evaluated by comparing the plane position resolutions of the test beams in different incident angles with and without the DOI correction. The test results show that the DOI determination could seamlessly integrate into our previously proposed the SOM-based plane coordinate estimation scheme to realize the high performance real-time three-dimensional position estimation for continuous crystal-based PET detectors.

关键词： Depth-of-interaction (DOI) nearest neighbor algorithm PET detector position estimation SOM neural network

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N-GRAM nearest neighbor algorithm FOR VOICE PASSWORD SYSTEM

N-GRAM NEAREST NEIGHBOR ALGORITHM FOR VOICE PASSWORD SYSTEM

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Wu Guo Zhao Zhang Yanhua Long Lirong Dai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China (USTC) China iFly Speech Lab Department of Electronic Engineering and Information Science University of Science and Technology of China (USTC) China

ISBN: (纸本)9781424442959

A specific issue in the voice password system is addressed in this paper: When the text content of target speaker's enrollment password has been already known by imposters, they can do a well-behaved impersonation using the same text content as the target speaker. This results in a much higher false acceptance than the traditional voice password system. N-gram based nearest neighbor algorithm is proposed here to improve the speaker detection accuracy. Furthermore, correlation coefficient is adopted as the distance measurement between two acoustic features instead of the traditional Euclidean distance. Experimental results show that the proposed method outperforms the DTW and GMM-UBM algorithms.

关键词： speaker recognition dynamic time warping nearest neighbor dynamic time warping speaker recognition nearest neighbor Voice distance measurement Passwords nearest neighbor algorithm Loudspeakers Euclidean distance

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Lighthouse: Accurate 802.11 Localization using Directional Beacon Fingerprinting

Lighthouse: Accurate 802.11 Localization using Directional B...

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International Conference on Location and GNSS (ICL-GNSS)

作者： Siddig, Abubakr Makki, Ahmed Bleakley, Chris Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland

ISBN: (纸本)9781479998586

In this paper, we propose a novel method for indoor location estimation using an unmodified mobile device (MD) in an IEEE 802.11 wireless local area network. In this method, Directional Beaconing is combined with Fingerprinting whereby the WiFi access points (APs) transmit a narrow beamwidth signal switching between a small number of fixed directions of emission (DOEs) and the MD records the received signal strength indicator (RSSI) for all in-range APs and all DOEs. These observations give a DOE-RSSI signatures at the MD for each AP. The location of the MD is determined using a K-nearest neighbor algorithm which finds the best matching DOE-RSSI signature in a prestored map. The method improves on previous omnidirectional fingerprinting methods by increasing the number of RSSI maps available per AP. A testbed network was deployed in an office building and its performance was evaluated. The results show a reduction in mean location error from 2.87 m to 1.64 m (43% improvement) for the proposed method compared with conventional omnidirectional RSSI fingerprinting, when using 3 APs in a positioning space of 12 m by 13.5 m.

关键词： Received Signal Strength Indicator Fingerprinting office buildings directional beacons location error Apus position estimation Lighthouses nearest neighbor algorithm wireless local area network (WLAN)

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Human Action Recognition by Employing DWT and Texture

Human Action Recognition by Employing DWT and Texture

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Thanikachalam, V. Thyagharajan, K. K. SSN Coll Engn Chennai Tamil Nadu India RMD Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9788132221357;9788132221340

Human action recognition is a very challenging task due to the great variability with which different people may perform the same action. It involves in the development of applications such as automatic monitoring, surveillance, and intelligent human-computer interfaces. We propose an action recognition scheme to classify human actions based on positive portion using template-based approach from a video. We first define the accumulated motion image (AMI) using frame differences to represent the spatiotemporal features of occurring actions. Then, the direction of motion is found out by computing motion history image (MHI). Texture and spatial information are extracted from AMI and MHI using (LBP) local binary pattern and (DWT) discrete wavelet transform, respectively. The detection of object and extraction of moving objects are done by feature extraction over LBP and DWT. The feature vectors are computed by employing the seven Hu moments. The system is trained using nearest neighbor classifier, and the actions are classified and labeled accordingly. The experiments are conducted on Weizmann dataset.

关键词： Accumulated motion image Discrete wavelet transform Human action recognition Local binary pattern Motion history image nearest neighbor algorithm

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Self-Organizing Map Neural Network-Based nearest neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2014年第5期61卷 2446-2455页

作者： Wang, Yonggang Li, Deng Lu, Xiaoming Cheng, Xinyi Wang, Liwei Univ Sci & Technol China Dept Modern Phys Hefei 230026 Anhui Peoples R China

Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance gamma interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 +/- 0.17 mm and 1.92 +/- 0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.

关键词： Continuous crystal PET detector nearest neighbor algorithm position of interaction SOM neural network

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Acquisition of Human Operation Characteristics for Kite-based Tethered Flying Robot using Human Operation Data

Acquisition of Human Operation Characteristics for Kite-base...

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IEEE International Conference on Fuzzy Systems

作者： Chiaki Todoroki Yasutake Takahashi Takayuki Nakamura Dept. of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui 3-9-1 Bunkyo Fukui Fukui 910-8507 Japan Department of Computer and Communication Sciences Faculty of Systems Engineering Wakayama University Sakaetani 930 Wakayama 640-8510 Japan

ISBN: (纸本)9781467374293

This paper shows human skill acquisition systems to control the kite-based tethered flying robot. The kite-based tethered flying robot has been proposed as a flying observation system with long-term activity capability[1]. It is a relatively new system and aimed to complement other information gathering systems using a balloon or an air vehicle. This paper shows some approaches of human operation characteristics acquisition based on fuzzy learning controller, k-nearest neighbor algorithm, and artificial neural network for the kite-based tethered flying robot using human operation data and their validity through computational simulation which we developed[2].

关键词： Human Operation Characteristics Kite-based Tethered Flying Robot Human Operation Data aerospace robotics operating performance observation system tethers plane Balloon Device Component nearest neighbor algorithm Artificial neural networks Human

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Learning from neighborhood for Classification with Local Distribution Characteristics

Learning from Neighborhood for Classification with Local Dis...

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International Joint Conference on Neural Networks

作者： Chengsheng Mao Bin Hu Manman Wang Philip Moore School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9781479919611

The k-nearest neighbor method generates predictions for a particular instance from its neighborhood. It is a simple but effective supervised method for classification. However, the traditional k-nearest neighbor algorithm using the majority voting rule for the class label usually loses a part of useful information in the neighborhood. This paper tries to learn from the neighborhood for more useful information for classification and proposes an improved version of k-nearest neighbor method by heuristically organizing the local distribution characteristics. Different from the traditional methods, the proposed method considers the neighborhood of a query sample from the perspective of local distribution and learns from the neighborhood for local distribution characteristics for classification. We analyze the impact of local distribution characteristics on classification and heuristically develop a formulation to estimate the membership degree, which indicates the level of membership of a query sample to each class;then the query sample is classified to the class which has the highest membership degree with respect to the query sample. Experiments have been conducted on several real data sets;the results support the conclusion that the proposed method is superior to the traditional voting k-nearest neighbor method and comparable with or better than several state-of-the-art methods in terms of classification performance and robustness.

关键词： Learning neighborhood Classification neighborhood nearest neighbor algorithm Learning Taxonomy neighbor

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