scholarly journals Survey on the relation between road freight transport, SCM and sustainable development

2019 ◽  
Vol 29 (2) ◽  
pp. 151-176
Author(s):  
Wiame Ech-Chelfi ◽  
Hammoumi El
Keyword(s):  
Sustainable Development ◽  
Kernel Methods ◽  
Error Probability ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Classification Problem ◽  
Model Error ◽  
Ensemble Clustering ◽  
Probability Of Error ◽  
Road Freight

In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. A probabilistic model of classification with the use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

scholarly journals Group approach to solving the tasks of recognition

2019 ◽  
Vol 29 (2) ◽  
pp. 177-192
Author(s):  
Yedilkhan Amirgaliyev ◽  
Vladimir Berikov ◽  
Lyailya Cherikbayeva ◽  
Konstantin Latuta ◽  
Kalybekuuly Bekturgan
Keyword(s):  
Kernel Methods ◽  
Probabilistic Model ◽  
Error Probability ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Classification Problem ◽  
Model Error ◽  
Ensemble Clustering ◽  
Group Approach ◽  
Probability Of Error

In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

THE ANALYTICAL MODEL OF NOISE IMMUNITY CALCULATION OF COMMUNICATION SYSTEMS WITH MULTI-POSITION TYPES OF MODULATION AND CONVOLUTIONAL CODING

2020 ◽  
Author(s):  
Э.Б. ЛИПКОВИЧ ◽  
А.А. СЕРЧЕНЯ
Keyword(s):  
Computer Simulations ◽  
Communication Systems ◽  
Error Probability ◽  
Noise Immunity ◽  
Convolutional Code ◽  
Analytical Models ◽  
Signal To Noise ◽  
Probability Of Error ◽  
Information Efficiency ◽  
Convolutional Coding

Получены математические модели расчета отношений сигнал/шум и несущая/шум, требуемые для обеспечения заданной вероятности ошибки на выходе декодера с «мягким» решением, без необходимости вычисления коэффициентов спектра сверточного кода и выполнения процедур компьютерного моделирования характеристик помехоустойчивости. Приведены расчетные выражения для определения исправляющей способности декодера, энергетического выигрыша от кодирования и информационной эффективности систем связи в зависимости от параметров многопозиционных видов модуляции, сверточного кодирования и вероятности ошибки в информационном бите. По полученным аналитическим моделям построены зависимости и дана оценка результатов исследований. Mathematical models are obtained for calculating signal-to-noise and carrier-to-noise ratios required to provide a given error probability at the decoder output with a “soft” solution and without calculating the convolutional code spectrum coefficients and performing computer simulations of noise immunity characteristics. Calculation expressions are given to determine the correcting ability of the decoder, the energy gain from coding, and the information efficiency of communication systems depending on the parameters of multi-position types of modulation, convolutional coding, and the probability of error in the information bit. Dependencies are constructed according to the obtained analytical models and the research results are evaluated.

Experiment of OCITN: Considering Appropriate Goal Images and Metric for One-Class Image Transformation Network

2021 ◽  
Author(s):  
Toshitaka Hayashi ◽  
Hamido Fujita
Keyword(s):  
Image Data ◽  
Classification Problem ◽  
Model Error ◽  
Input Image ◽  
Training Data ◽  
Distance Metric ◽  
Image Transformation ◽  
Image Entropy ◽  
Image Metrics ◽  
One Class Classification

One-class classification (OCC) is a classification problem where training data includes only one class. In such a problem, two types of classes exist, seen class and unseen class, and classifying these classes is a challenge. Besides, One-class Image Transformation Network (OCITN) is an OCC algorithm for image data. In which, image transformation network (ITN) is trained. ITN aims to transform all input image into one image, namely goal image. Moreover, the model error of ITN is computed as a distance metric between ITN output and a goal image. Besides, OCITN accuracy is related to goal image, and finding an appropriate goal image is challenging. In this paper, 234 goal images are experimented with in OCITN using the CIFAR10 dataset. Experiment results are analyzed with three image metrics: image entropy, similarity with seen images, and image derivatives.

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

scholarly journals Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yidong Tang ◽  
Shucai Huang ◽  
Aijun Xue
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Kernel Method ◽  
Matching Pursuit ◽  
Classification Problem ◽  
Kernel Space ◽  
Hypothesis Model ◽  
Extended Surface ◽  
Number Of Classes ◽  
Binary Hypothesis

The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

Hyperspectral Image Classification Based on Artificial Immune System

2012 ◽  
Vol 500 ◽  
pp. 806-812 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Immune System ◽  
Image Classification ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Hyperspectral Image Classification ◽  
Spectral Dimensionality ◽  
Hyperspectral Remote Sensing Images ◽  
Insight Into

The high spectral dimensionality in hyperspectral images causes the reduction of accuracy for common statistical classification methods in these images. Hence the generation and implementation of more complicated methods have gained great importance in this field. One of these methods is the Artificial Immune Systems which is inspired by natural immune system. Despite its great potentiality, it is rarely utilized for spatial sciences and image classification. In this paper a supervised classification algorithm with the application of hyperspectral remote sensing images is proposed. In order to gain better insight into its capability, its accuracy is compared with Artificial Neural Network. The results show better image classification accuracy for the Artificial Immune method.

Error Probability Analysis of Time-Hopping Biorthogonal Pulse Position Modulation UWB Systems with a RAKE Receiver over Indoor Multi-Path Fading Channels

2012 ◽  
Vol 433-440 ◽  
pp. 2011-2018
Author(s):  
Hao Zhang ◽  
Wei Shi ◽  
Ting Ting Lv ◽  
T. Aaron Gulliver
Keyword(s):  
Fading Channels ◽  
Error Probability ◽  
Channel Model ◽  
Rake Receiver ◽  
Low Complexity ◽  
Probability Analysis ◽  
Ultra Wideband ◽  
Pulse Position Modulation ◽  
Probability Of Error ◽  
Time Hopping

This paper presents the error probability analysis of Time-Hopping Biorthogonal Pulse Position Modulation (TH-BPPM) ultra-wideband (UWB) systems with a RAKE receiver over indoor multi-path fading channels. UWB signals suffer from severe multi-path interference when employed in an indoor fading environment. A RAKE receiver can be used to improve the performance of UWB systems. TH-BPPM has attracted much attention in recent years due to its many advantages, such as low probability of error and low complexity. In this paper, the IEEE 802.15.3a indoor channel model is employed to analyze the performance of TH-BPPM UWB systems with different RAKE receivers. The bit error rate (BER) of ARake, PRake, and SRake TH-BPPM UWB systems is derived. The results indicate that ARake has the best performance, SRake is better than PRake when the number of fingers is same.

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