2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第3页

On vehicle state tracking for long-term carpark video surveillance 基于车辆状态跟踪的停车场长期视频监控

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120638

R. Lim, Clarence Weihan Cheong, John See, I. Tan, L. Wong, Huai-Qian Khor

引用次数: 5

Sparse signal reconstruction of compressively sampled signals using smoothed ℓ0-norm 利用光滑的0-范数对压缩采样信号进行稀疏重构

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120580

J. Shah, Hassaan Haider, K. Kadir, Sheroz Khan

{"title":"Sparse signal reconstruction of compressively sampled signals using smoothed ℓ0-norm","authors":"J. Shah, Hassaan Haider, K. Kadir, Sheroz Khan","doi":"10.1109/ICSIPA.2017.8120580","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120580","url":null,"abstract":"Compressed Sensing is a novel sampling technique that can be used to faithfully recover sparse signals from fewer measurements than those proposed by the Nyquist theorem. A simple and intuitive measure of sparsity in a signal is ℓ0-norm. However, the ℓ0-norm function does not satisfy all the axiomatic properties of a true mathematical norm. The discrete and discontinuous nature of ℓ0-norm poses many challenges in its applications to recover sparse signals from their subsampled measurements. This paper presents, a novel mathematical function that can be used to closely approximate the ℓ0-norm. The proposed function is smooth and differentiable that allows gradient based algorithms to be used in the reconstruction of sparse signals. We use the proposed approximation along with steepest ascent method to develop a complete sparse signal recovery algorithm for the compressed sensing framework. Experimental results have shown that the proposed recovery algorithm outperforms the conventional SL0 method in terms of reconstruction accuracy such as Mean Square Error (MSE) and Signal-to-Noise Ratio (SNR).","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116196817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Real-time model predictive control for nonlinear gas pressure process plant 非线性气体压力过程装置的实时模型预测控制

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120625

E. Hasan, R. Ibrahim, Kishore Bingi, S. Hassan, Syed Faizan-ul-Haq Gilani

{"title":"Real-time model predictive control for nonlinear gas pressure process plant","authors":"E. Hasan, R. Ibrahim, Kishore Bingi, S. Hassan, Syed Faizan-ul-Haq Gilani","doi":"10.1109/ICSIPA.2017.8120625","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120625","url":null,"abstract":"Nonlinear behaviour of the systems happens to be a common problem in industrial processes. They cause a large amount of time, resources and efforts to be utilized in order to deal with them. A Major hurdle in Nonlinear Industrial Processes is system modeling. Due to this reason, several methods and techniques have been designed and developed in order to improve the overall control performance in industrial process control. Model based controllers have been developed and implemented on various applications with promising results. Their main benefit is they can identify and tune unknown system parameters in real-time. This paper focuses on real-time controller development and its implementation on Gas Pressure Process Plant using MPC. MPC is considered to be one of the robust and effective controllers due to impressive control performance in different applications previously. MPC makes use of a model for system identification and based upon that, it can dynamically send next control move for the system. This research work incorporates State-Space Model for unknown system-parameter identification. The identified parameters will be utilized by MPC for control law development. The proposed methodology is validated by real-time experimental results on the aforementioned system.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120951366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Classification of benign and malignant tumors in histopathology images 组织病理学图像中良恶性肿瘤的分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120587

Afiqah Abu Samah, M. F. A. Fauzi, Sarina Mansor

引用次数: 23

Optimal feature subset selection for fuzzy extreme learning machine using genetic algorithm with multilevel parameter optimization 基于多级参数优化遗传算法的模糊极值学习机特征子集优选

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120652

A. Kale, S. Sonavane

{"title":"Optimal feature subset selection for fuzzy extreme learning machine using genetic algorithm with multilevel parameter optimization","authors":"A. Kale, S. Sonavane","doi":"10.1109/ICSIPA.2017.8120652","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120652","url":null,"abstract":"The crucial objective of this paper is to design a hybrid model of the genetic algorithm for fuzzy extreme learning machine classifier (GA-FELM), which selects an optimal feature subset by using the multilevel parameter optimization technique. Feature subset selection is an important task in pattern classification and knowledge discovery problems. The generalization performance of the system is not only depending on optimal features but also dependent upon the classifier (learning algorithm). Therefore, it is an important task to select a fast and efficient classifier. Research efforts have affirmed that extreme learning machine (ELM) has superior and accurate classification ability. However, ELM is failed to handle the uncertain data. One of the alternative solutions is fuzzy-ELM, which combines the advantages of fuzzy logic and ELM. GA-FELM is able to handle curse of dimensionality problem, optimization problem and weighted classification problem with maximizing classification accuracy by minimizing the number of features. In order to validate the efficiency of GA-FELM, the comparative performance is evaluated by using three different approaches viz. 1. ELM and GA-ELM 2. GA-ELM and GA-FELM 3. GA-FELM and GA-existing classifier. The result analysis shows that classification accuracy is improved with 9% while reducing 62% features.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132384930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Deep-learning: A potential method for tuberculosis detection using chest radiography 深度学习:一种利用胸部x线摄影检测结核病的潜在方法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120663

Rahul Hooda, S. Sofat, Simranpreet Kaur, Ajay Mittal, F. Mériaudeau

引用次数: 70

Classification of fMRI data using support vector machine and convolutional neural network 基于支持向量机和卷积神经网络的fMRI数据分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120630

R. Zafar, A. Malik, Aliyu Nuhu Shuaibu, M. J. U. Rehman, S. Dass

{"title":"Classification of fMRI data using support vector machine and convolutional neural network","authors":"R. Zafar, A. Malik, Aliyu Nuhu Shuaibu, M. J. U. Rehman, S. Dass","doi":"10.1109/ICSIPA.2017.8120630","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120630","url":null,"abstract":"In recent years convolutional neural network have obtained more popularity because of its progressive performance for different applications especially for object recognition. In neuroimaging, data varies from person to person and condition to condition so it is always a challenging job to model the brain data. Any analysis in neuroimaging is also dependent on the quality of data and currently, functional magnetic resonance imaging is considered as the best among all techniques. It is most reliable and popular modality to measure the brain activity patterns. In fMRI, region of interest is a common method of analysis in which data is taken from a specific brain region based on the structural or functional information. In this study, convolutional neural network is applied to the significant voxels obtained through the t-contrast of the design matrix during the ROI analysis. Data is taken against two conditions and 1000 significant voxels with highest absolute values are taken for each condition for further analysis. During the proposed method, analysis is performed using convolutional neural network along with ROI analysis. Support vector machine is used in the classification of both methods; ROI and proposed methods. In conclusion, it is shown that the features extracted through convolutional neural network can provide better significant results compared to the other one.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"106 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Mixed emotions in multi view face emotion recognition 多视角人脸情绪识别中的混合情绪

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120643

F. Goodarzi, F. Rokhani, M. Saripan, M. Marhaban

引用次数: 4

Mammogram classification using deep learning features 使用深度学习特征的乳房x线照片分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120660

S. J. S. Gardezi, M. Awais, I. Faye, F. Mériaudeau

引用次数: 32

Fruit maturity estimation based on fuzzy classification 基于模糊分类的水果成熟度评价

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120574

Rija Hasan, S. Monir

引用次数: 6