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Non-Homogeneous Hidden Markov Chain Models for Wavelet-Based Hyperspectral Image Processing

Non-Homogeneous Hidden Markov Chain Models for Wavelet-Based...

引用

51st IEEE Annual Allerton Conference on Communication, Control, and Computing

作者： Duarte, Marco F. Parente, Mario Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

ISBN: (纸本)9781479934096

We consider the use of non-homogeneous Markov chain (NHMC) models for wavelet transformations of hyper-spectral signatures to generate features for signal processing purposes. Inspired by the use of hidden Markov trees for natural images, the NHMC model enables the characterization of absorption bands and other structural features of mineral spectra that are used by experts in tasks like classification and unmixing, primarily in an ad-hoc fashion. We show that NHMC models can successfully identify and capture the information in a spectral signature dataset that can be exploited by standard classification algorithms to identify and differentiate spectral families. We also identify several metrics that can help determine whether each spectral band is informative to classification in a multiscale fashion.

关键词： Hidden Markov models Markov chain hyperspectral imagery spectral bands Absorption bands signal processing Metronidazole Professional personnel Image processing Religious Missions Spectrum signatures isostructuralism sorting algorithm

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FCI: Feature Cross and User Interest Network 1

引用

10th CCF Big Data Conference (BigData)

作者： Lei, Dejun Southwest Petr Univ Sch Comp Sci 8 Xindu Ave Chengdu 610599 Sichuan Peoples R China

ISBN: (数字)9789811983313

ISBN: (纸本)9789811983306;9789811983313

Recommendation systems are widely used on Internet platforms and the Recommendation system is one of the most mature scenarios for data mining applications. Ranking algorithms in recommender systems are the most effective means to increase user clicks. At present, the recommendation system ranking algorithm is mainly divided into two categories. One is based on data mining, such as user features, product features, and scene features and crosses these functions manually or automatically. The second method, by mining the user's behavior sequence, obtains the user's interests and displays the product to the user. The first approach does not mine useful behavioral data. The second method cannot effectively perform feature crossover and loses some crossover information between users and products. This paper proposes a new ranking method for recommender systems. Feature Cross and User Interest Network FCI. It can efficiently and automatically discover cross features based on users, products, and scenarios. At the same time, mining the historical behavior information of users. In this algorithm, firstly our put-forward model resembles a wide-depth structure. Wide structure puts feature intersection network, deep structure puts user historical behavior mining network. Secondly experimental comparisons are made by changing different feature intersection networks and user behavior sequence networks. Finally, by adding the pre-training vector of the product, compare different pre-training methods. This approach greatly improves the CTR in practical recommender systems.

关键词： Recommended system Behavior mining Feature-cross sorting algorithm Recommendation model

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Intellectual Scaling in a Distributed Cloud Application Architecture: a Message Classification algorithm

Intellectual Scaling in a Distributed Cloud Application Arch...

引用

2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP)

作者： Iakushkin, Oleg St Petersburg State Univ 7-9 Univ Skaya Nab St Petersburg 199034 Russia

ISBN: (纸本)9781467376983

The service systems deployed in a cloud environment have a volatile business logic which cannot be fully described by messaging patterns now available. Firstly, the work of distributed service systems is heavily influenced by the type, format, topic, and content of messages. Secondly, the existing middleware solutions for message exchange have a very narrow functionality focused exclusively on message passing between the nodes of a distributed system. They pay no attention to the features of the messages sent. However, scaling in hybrid and geo-distributed cloud solutions requires a more comprehensive approach. So does the analysis of the state of system modules. This paper offers an algorithm allowing to classify messages which are sent via the same protocol but have various content and do not correspond to a pre-set format. We provide an example of using such algorithm in middleware architecture. The major advantages and drawbacks of the algorithm are described with regard to a distributed cloud system.

关键词： Messages CLOUDS Clouds program architecture information exchange sorting algorithm water service reliability Distributed Systems message passing Scale formation Middleware intellectuals

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A Target Classification algorithm Based on Transportation Sensing Network

A Target Classification Algorithm Based on Transportation Se...

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Workshop on Power Electronics and Intelligent Transportation System

作者： Cui Xun-xue Qiu Guo-xin Zeng Jian-qin Xing Li-jun Liu Qi New Star Res Inst Appl Technol Hefei 230031 Peoples R China

ISBN: (纸本)9780769533421

Target classification is an important enabling technology for the monitoring task in transportation sensing networks. In the paper the magnetic signal and seismic acceleration signal are collected, analyzed and transmitted for a mobile road target. A classification algorithm based on the sensor network is proposed, which adopts a peak and valley pattern of hybrid detection signals from different sensors. The hardware equipment system of sensor node is devised. The terminal nodes are small with low power consumption so that the transportation sensing network is easy to deploy. The advantage of the algorithm lies on its sorting exactness of several kinds of targets. The results from many field tests have been shown that it is capable of identifying mobile targets on some roads in intelligent transportation system.

关键词： sorting algorithm field experimentation end node Sensor networks Targets transport Intelligent transportation systems Road low power consumption valleys TARGET CLASSIFICATION

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Emotions in speech -: Experiments with prosody and quality features in speech for use in categorical and dimensional emotion recognition environments

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International Conference on Natural Language Processing and Knowledge Engineering

作者： Borchert, M Düsterhöft, A Univ Business Technol & Design Hsch Wismar Dept EE&CS D-23952 Wismar Germany

ISBN: (纸本)0780393619

This paper focuses on features in speech and classification algorithms for using them in emotion recognition software. We illustrate a new approach concentrated on analyzing speech quality features. The quality features are formants, spectral energy distribution in different frequency bands, harmonics-to-noise ratio (in different frequency bands) and irregularities (jitter, shimmer). Some papers 131, 141 show that there is a relationship between quality features and the valence axis. This paper deals with dimensional approach to classify emotions. Therefore, mainly quality features will be taken for the valence axis to classify emotions and mainly prosody features will be taken for the arousal axis. Because our experiments show that single emotion recognition rates are up to 90 percent and the recognition rates for speaker independent recognition is about 70% for all classification algorithm, it seems that quality features are more appropriate to differentiate emotions with the same arousal and different valence levels in a dimensional approach. A prototypical emotion recognition software is implemented which is actually, tested for analyzing the mood of customers in call centers.

关键词： call centres emotion recognition signal classification speech recognition call centers categorical emotion recognition dimensional emotion recognition software emotion classification algorithms harmonics-to-noise ratio prosody features speaker independen Call Centers emotion recognition Emotions signal classification Speech prosody sorting algorithm Frequency bands DIMENSIONAL Harmonic noise

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Cascaded H-bridge Converter Integrated with Split Batteries and with Multilevel AC Output for Household Applications 39

Cascaded H-bridge Converter Integrated with Split Batteries ...

引用

Applied Power Electronics Conference and Exposition (APEC)

作者： Alimawi, Madhat Eskandari, Reyhaneh Venugopal, Prasanth Soeiro, Thiago Batista Univ Twente Fac EEMCS Enschede Netherlands Univ Nottingham Fac Engn Nottingham England

ISBN: (纸本)9798350316643

This study presents a 6-module cascaded Hbridgemultilevel converter design for AC split batteries in residential applications. It incorporates sorting-based balancing and level-shifted modulation for reduced harmonic distortion and current ripple. Two current controllers, PR and dq controllers, are employed for grid synchronization and quality control, with a focus on comparing their performance in terms of harmonic distortion, settling time, and steady-state error. Simulation results validate the effectiveness of sub-batteries balancing method during charging and discharging, and the stability of both controllers against the 5th and 7th harmonic distortion. Additionally, an experimental prototype with 6 battery emulators confirms the feasibility of the system and sorting algorithm.

关键词： Cascaded H-bridge AC split battery balancing current controller sorting algorithm

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Average Likelihood Method for Classification of CDMA 33

Average Likelihood Method for Classification of CDMA

引用

33rd Annual IEEE Military Communications Conference on Affordable Mission Success - Meeting the Challenge (MILCOM)

作者： Irizarry, Alfredo Vega Fam, Adly Air Force Res Lab Rome NY 13441 USA SUNY Buffalo New York NY USA

ISBN: (纸本)9781479967704

A new algorithm for classifying Direct Sequence Code Division Multiple Access (DS-CDMA) signals in additive white Gaussian noise (AGWN) is proposed based on the average likelihood (AL) function. The AL is express in terms of the code length and the number of active users. These parameters constitute the hypothesis under test. While the AL function is expressed in an analytical form, the implementation requires knowledge on the spreading codes that acchive a low Total Squared Correlation (TSC) value. This complexity can be simplified by developing a hard decision scheme which only requires knowledge on matrices that achieve the lowest TSC. Our study cases include binary and multiclass classification using full loaded DS-CDMA with knowledge onf the chip period. Code lengths of powers of 2 are used as a proof of concept.

关键词： AWGN code division multiple access codes matrix algebra signal classification spread spectrum communication AGWN AL function method DS-CDMA signal classification additive white Gaussian noise average likelihood function method binary classification chip period code length direct sequence code division multiple access signal classification hard decision scheme low total squared correlation value multiclass classification number-of-active users spreading codes Classification algorithms Complexity theory Correlation Error correction Error correction codes Multiaccess communication Vectors AWGN Code division multiple access code length spreading code Multi access systems Error correction codes sorting algorithm Spread spectrum communication signal classification Complexity theory Direct-sequence code-division multiple access matrix algebra error correction

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Continuous Authentication using Mouse Dynamics 12

Continuous Authentication using Mouse Dynamics

引用

12th Annual International Conference of the Biometrics-Special-Interest-Group (BIOSIG) of the Gesellschaft-fur-Informatik (GI) e V

作者： Mondal, Soumik Bours, Patrick Gjovik Univ Coll NISlab N-2820 Gjovik Norway

ISBN: (纸本)9783885796060

In this paper, we demonstrate a new way to perform continuous authentication using Mouse Dynamics as the behavioural biometric modality. In the proposed scheme, the user will be authenticated per mouse event performed on his/her system. We have used a publicly available mouse dynamics dataset and extracted per event features suitable for the proposed scheme. In this research, we have used the mouse dynamics data of 49 users and evaluated the system performance with 6 machine learning algorithms. In this approach, the genuine user has never been classified as an impostor throughout a full session whereas the average number of mouse actions an impostor could perform before detection is 94 from the best classification algorithm with a person based threshold.

关键词： Fraud Mus Mouse System performance sorting algorithm dynamics Authentication Persons Machine learning algorithms

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Structure selection in synthetic aperture radar scattering models 25

Structure selection in synthetic aperture radar scattering m...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Akyildiz, Y Moses, RL Ohio State Univ Dept Elect Engn Columbus OH 43210 USA

ISBN: (纸本)0780362934

High-frequency models for radar backscatter can include components with different structures that correspond to different physical scattering mechanisms on an object. We consider the problem of structure selection for an attributed scattering center model that includes both localized and distributed scattering terms. We propose three structure classification algorithms, and compare their performance. We show that a threshold test on the estimated length parameter performs as well as a GLRT test, but is computationally more efficient. A computationally fast image-based test is shown to perform as well as the GLRT and length-based tests for scattering center lengths greater than twice the crossrange resolution of the measurements.

关键词： broadcasting Radar Backscatter Synthetic aperture radar High frequency sorting algorithm WELLS

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To Be Or Not To Be IID: Can Zipf's Law Help?

To Be Or Not To Be IID: Can Zipf's Law Help?

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IEEE International Symposium on Technologies for Homeland Security (HST)

作者： Behe, Leo Wheeler, Zachary Nelson, Christie Knopp, Brian Pottenger, William M. Lehigh Univ Bethlehem PA 18015 USA Grove City Coll Grove City PA USA Rutgers State Univ Piscataway NJ USA Montana Tech Univ Butte MT USA

ISBN: (纸本)9781479917372

Classification is a popular problem within machine learning, and increasing the effectiveness of classification algorithms has many significant applications within industry and academia. In particular, focus will be given to Higher-Order Naive Bayes (HONB), a relational variant of the famed Naive Bayes (NB) statistical classification algorithm that has been shown to outperform Naive Bayes in many cases [1,10]. Specifically, HONB has outperformed NB on character n-gram based feature spaces when the available training data is small [2]. In this paper, a correlation is hypothesized between the performance of HONB on character n-gram feature spaces and how closely the feature space distribution follows Zipf's Law. This hypothesis stems from the overarching goal of ultimately understanding HONB and knowing when it will outperform NB. Textual datasets ranging from several thousand instances to nearly 20,000 instances, some containing microtext, were used to generate character n-gram feature spaces. HONB and NB were both used to model these datasets, using varying character n-gram sizes (2-7) and dictionary sizes up to 5000 features. The performances of HONB and NB were then compared, and the results show potential support for our hypothesis: namely, the results support the hypothesized correlation for the Accuracy and Precision metrics. Additionally, a solution is provided for an open problem which was presented in [1], giving an explicit formula for the number of SDRs from k given sets, which has connections to counting higher-order paths of arbitrary length, which are important in the learning stage of HONB.

关键词： Eigenvector sorting algorithm Bayes CHARACTER Character

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