Channel Estimation Based on Extreme Learning Machine for High Speed Environments

2016 ◽  
pp. 159-167 ◽  
Author(s):  
Fang Dong ◽  
Junbiao Liu ◽  
Liang He ◽  
Xiaohui Hu ◽  
Hong Liu
Keyword(s):  
Channel Estimation ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Learning Machine

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

scholarly journals FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 6551-6557
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Bench Mark ◽  
Hardware Platform ◽  
Learning Speed ◽  
Field Programmable ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Elm Classifier ◽  
Short Event

To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.

scholarly journals Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 968
Author(s):  
Eduardo Salazar ◽  
Cesar A. Azurdia-Meza ◽  
David Zabala-Blanco ◽  
Sandy Bolufé ◽  
Ismael Soto
Keyword(s):  
Channel Estimation ◽  
Extreme Learning Machine ◽  
Operation Time ◽  
Communication Link ◽  
Vehicular Communications ◽  
Time Varying Frequency ◽  
Urban Scenario ◽  
Equalization Technique ◽  
Learning Machine ◽  
Channel Estimation And Equalization

Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications.

scholarly journals Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1204 ◽  
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Imbalanced Data ◽  
Classification Performance ◽  
Complex Data ◽  
Minority Class ◽  
Diagnosis Model ◽  
Learning Machine ◽  
Hidden Layer

To quickly and effectively identify an axle box bearing fault of high-speed electric multiple units (EMUs), an evolutionary online sequential extreme learning machine (OS-ELM) fault diagnosis method for imbalanced data was proposed. In this scheme, the resampling scale is first determined according to the resampling empirical formulation, the K-means synthetic minority oversampling technique (SMOTE) method is then used for oversampling the minority class samples, a method based on Euclidean distance is applied for undersampling the majority class samples, and the complex data features are extracted from the reconstructed dataset. Second, the reconstructed dataset is input into the diagnosis model. Finally, the artificial bee colony (ABC) algorithm is used to globally optimize the combination of input weights, hidden layer bias, and the number of hidden layer nodes for an OS-ELM, and the diagnosis model is allowed to evolve. The proposed method was tested on the axle box bearing monitoring data of high-speed EMUs, on which the position of the axle box bearings was symmetrical. Numerical testing proved that the method has the characteristics of faster detection and higher classification performance regarding the minority class data compared to other standard and classical algorithms.

Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems

2019 ◽  
Vol 23 (7) ◽  
pp. 1276-1279 ◽  
Author(s):  
Jun Liu ◽  
Kai Mei ◽  
Xiaochen Zhang ◽  
Dongtang Ma ◽  
Jibo Wei
Keyword(s):  
Channel Estimation ◽  
Extreme Learning Machine ◽  
Ofdm Systems ◽  
Learning Machine ◽  
Channel Estimation And Equalization

scholarly journals Multimodal Emotion Recognition Using the Symmetric S-ELM-LUPI Paradigm

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 487
Author(s):  
Lingzhi Yang ◽  
Xiaojuan Ban ◽  
Michele Mukeshimana ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Information Source ◽  
Privileged Information ◽  
Learning Methods ◽  
Multimodal Emotion Recognition ◽  
Learning Machine ◽  
Learning Using Privileged Information

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable.

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