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Noncanonical and Strongly Disallowed Conformations of the Backbone in Polypeptide Chains of Globular Proteins

Biophysics (Russian Federation) 2018年第2期63卷 149-153页

作者： Torshin, I. Yu. Batyanovskii, A.V. Uroshlev, L.A. Esipova, N.G. Tumanyan, V.G. Department of Chemistry Moscow State University Moscow 119991 Russian Federation Institute of Biophysics and Cell Engineering National Academy of Sciences of Belarus Minsk 220072 Belarus Engelhardt Institute of Molecular Biology Russian Academy of Sciences Moscow 119991 Russian Federation

An analog of the Ramachandran map was drawn, a new representation proposed, and thorough analysis performed using modern recognition and classification methods. Very large maps with a density of more than 50 million dots were created based on the data sets derived from the latest releases of globular protein- structure data banks. A, B, B', C, and D regions that correspond to strongly disallowed conformations were defined and found to occupy 25% of the plot area. A region of noncanonical conformations was determined by subtracting strongly disallowed and permitted conformation regions from the total plot area. Arguments are provided to support the new classification of backbone conformations of the protein polypeptide chain. © 2018, Pleiades Publishing, Inc.

关键词： conformational analysis globular proteins noncanonical conformations permitted Ramachandran map recognition and classification statistical and cluster analyses strongly disallowed

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Suitability of Feature Extraction Methods in recognition and classification of Grains, Fruits and Flowers

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INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2011年第1期7卷 1-28页

作者： Anami, Basavaraj S. Savakar, Dayanand G. K.L.E. Institute of Technology India B.L.D.E.A's Vachana Pitamaha Dr. P.G. Halakatti College of Engineering and Technology India

This paper presents the suitability of feature extraction methods for the identification and classification of certain agriculture and horticulture crops. Primarily, agriculture/horticulture crops are recognized based on their shape, size, color, texture and the like. When crops exhibit different shapes and sizes, it is customary to choose the shape and size as the basic features. Certain crops are easily identified simply by color;for example, with crops like jowar, ground nut, pomegranate and mango, color becomes the discriminating feature. We have considered color as one of the features in this study. Some agriculture/horticulture crops have overlapping colors, such as wheat and ground nut or mango and orange. When we consider the bulk samples of such grains or fruits, the surface patterns vary from crop to crop. In such cases, the texture becomes ideal for recognition. Hence, we have obtained morphological features, like shape and size, color and textural features, of the image samples to recognize, classify and grade the agriculture/horticulture crops.

关键词： color features textural features morphological features recognition and classification

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Feature Extraction and recognition of Rotational Target under the Sea Background 13

Feature Extraction and Recognition of Rotational Target unde...

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13th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Zhu, Bing Gao, Weixin Qin, Yali Li, Wenfeng Kong, Xianglong Xian Shiyou Univ Sch Elect Engn 18 Dianzi Er Rd Xian Shannxi Peoples R China 210 Inst China Aerosp Sci & Technol Corp 8 Dianzi Yi Rd Xian Shannxi Peoples R China Shanghai Inst Satellite Engn 3666 Yuanjiang Rd Shanghai Peoples R China

ISBN: (纸本)9789897581984

Considering the impact of sea clutter on target classification and recognition, a method based on RBF is proposed to restrain the actual sea clutter, which can be converted the sea clutter into random noise. After denosing, a S transform time-frequency approach is used to obtain the two time-frequency distribution images. They are helicopter and propeller aircraft images with nosie. Then extracted the invariant moment features of images for target recognition. The simulation results have shown an average accuracy of 85%, which validates the effectiveness of this method.

关键词： Sea Clutter Restraint Time-frequency Transform Invariant Moment Features Rotate Plan recognition and classification

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Detection of oil spills in a complex scene of SAR imagery

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Science China(Technological Sciences) 2014年第11期57卷 2204-2209页

作者： FENG Jing CHEN He BI FuKun LI JunXia WEI Hang School of Information and Electronics Beijing Institute of Technology College of Information Engineering North China University of Technology

We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false *** is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark ***-based and edge-based segmentations are done to extract oil spills from bright and dark sea,*** proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional *** on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false *** experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.

关键词： SAR image oil spills detection dark spot extraction recognition and classification false alarm rejection

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Feature Extraction and recognition Based on the Biological Analysis

Feature Extraction and Recognition Based on the Biological A...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Zhao, Wencang Wang, Fan Qingdao Univ Sci & Technol Automat & Elect Engn Coll Qingdao 266042 Peoples R China

ISBN: (纸本)9783038350125

In this paper the extracted features including rectangularity, elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter, the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

关键词： Stored Product Pests Biological Analysis Feature Extraction BP Neural Networks Support Vector Machines recognition and classification

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Neural networks that perform recognition using generative error may help fill the "Neuro-Symbolic Gap"

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BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 2013年 3卷 6-12页

作者： Achler, Tsvi Los Alamos Natl Lab Div Theoret T5 Cognit Comp Grp Los Alamos NM 87545 USA

Matching the brain's ability to quickly incorporate new information and have it immediately available for logic and inference remains difficult using feedforward neural network recognition models. Feedforward neural network weights are difficult to modify and are sub-symbolic: they cannot be easily used for logic and reasoning. This work shows that by implementing neural network dynamics differently, during the testing phase instead of the training phase, pattern recognition can be performed using more flexible and symbolically-relevant weights. This advancement is an important step towards the merging of neural-symbolic representations, flexibility, memory, and reasoning with pattern recognition. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Feedforward neural networks Generative error Generative model Symbolic representations recognition and classification Supervised recognition

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Acoustic Signature recognition Technique for Human-Object Interactions (HOI) in Persistent Surveillance Systems

Acoustic Signature Recognition Technique for Human-Object In...

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Conference on Signal Processing, Sensor Fusion, and Target recognition XXII

作者： Alkilani, Amjad Shirkhodaie, Amir Tennessee State Univ Dept Mech & Mfg Engn Ctr Excellence Battlefield Sensor Fus Nashville TN 37209 USA

ISBN: (纸本)9780819495365

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

关键词： Human Object Interactions (HOI) Acoustic Sounds recognition and classification Spectral Analysis Principal Component Analysis k-d Tree Classifier Random Forests Classifier and Persistent Surveillance System

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Feature Extraction for Digital Object in Pervasive Computing Environment

Feature Extraction for Digital Object in Pervasive Computing...

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3rd International Conference on Pervasive Computing and Applications

作者： Song, Ruixia Wang, Xiaochun Li, Jian North China Univ Tech Coll Sci Beijing Peoples R China Beijing Forestry Univ Coll Sci Beijing Peoples R China Macau Univ Sci & Tech Fac Informat Tech Macau Peoples R China

ISBN: (纸本)9781424420209

In view of the multiplicity of the information in pervasive computing environment, this paper propose a new kind of mathematical model-V-system with multi -resolution property, with which we can do spectrum analysis for the whole multimedia data. By doing global analysis to two and three dimensional digital geometric objects, the global characteristics of the corresponding digital information is obtained, and the digital information is then accurately, reconstructed. Further, recognition and classification on the geometric information can be done.

关键词： Feature extraction V-system Frequency spectrum analysis recognition and classification

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A new approach for intra-pulse modulation recognition

A new approach for intra-pulse modulation recognition

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CIE International Conference on Radar

作者： Wang Fenghua Huang Zhitao Zhou Yiyu Jiang Wenli NUDT Sch Elect Sci & Engn Changsha 410073 Peoples R China

ISBN: (纸本)9780780395824

Based on first-order and second-order phase difference, an algorithm for the modulation recognition is developed in this paper. A method based on instanteous autocorrelation which can obtain the first-order phase difference is given in this paper, which needn't reveal the phase ambiguity. In order to improve the performance of recognition, N-overlapping phase difference is developed. Several simulations are also conducted and the simulation results indicate the efficiency of the given method.

关键词： intra-pulse modulation N-overlapping phase difference instanteous autocorrelation recognition and classification

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