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9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Ghita, Cristian Raicu, Razvan Doru Pantelimon, Brandusa Univ Politehn Bucuresti Bucharest Romania Mil Tech Acad Bucharest Romania

ISBN: (纸本)9781479975143

The purpose of this paper is to analyze the use of the fixed- pointed algorithm fastica on a mix of four voices with the aim of separating each of the voices. Two experiments where performed, one with real data with artificial mixing and the other one with real data with real mixing. Following these experiments, discussions were held and some conclusions were drawn.

关键词： sound source separation fastica algorithm

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Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 11-20页

作者： Hu, Ruochen Chen, Xiang Zhang, Xu Chen, Xun Univ Sci & Technol China USTC Sch Informat Sci & Technol Hefei 230052 Peoples R China

To reduce the bad effect of electrode shifts on myoelectric pattern recognition, this paper presents an adaptive electrode calibration method based on core activation regions of muscles. In the proposed method, the high-density surface electromyography (HD-sEMG) matrix collected during hand gesture execution is decomposed into source signal matrix and mixed coefficient matrix by fast independent component analysis algorithm firstly. The mixed coefficient vector whose source signal has the largest two-norm energy is selected as the major pattern, and core activation region of muscles is extracted by traversing the major pattern periodically using a sliding window. The electrode calibration is realized by aligning the core activation regions in unsupervised way. Gestural HD-sEMG data collection experiments with known and unknown electrode shifts are carried out on 9 gestures and 11 participants. A CNN+LSTM-based network is constructed and two network training strategies are adopted for the recognition task. The experimental results demonstrate the effectiveness of the proposed method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing user training burden of myoelectric control systems. With the proposed electrode calibration method, the overall gesture recognition accuracies increase about (5.72 similar to 7.69)%. In specific, the average recognition accuracy increases (13.32 similar to 17.30)% when using only one batch of data in data diversity strategy, and increases (12.01 similar to 13.75)% when using only one repetition of each gesture in model update strategy. The proposed electrode calibration algorithm can be extended and applied to improve the robustness of myoelectric control system.

关键词： Electrodes Muscles Calibration Gesture recognition Data collection Training Electrode calibration core activation region of muscles HD-sEMG fastica algorithm myoelectric pattern recognition

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Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

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SENSORS 2022年第10期22卷 3705-3705页

作者： Hao, Jingyu Yang, Yuyao Zhou, Zhuhuang Wu, Shuicai Beijing Univ Technol Fac Environm & Life Dept Biomed Engn Beijing 100124 Peoples R China

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (fastica) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

关键词： fetal ECG signal extraction fastica algorithm singular value decomposition wavelet mode maximum method QRS waves

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Order and phase ambiguities correction in the ICA based separation of speech signals

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2020年第2期23卷 459-469页

作者： Uddin, Z. Orakzai, F. A. Qamar, A. COMSATS Univ Islamabad Wah Campus Rawalpindi Pakistan

Independent component analysis (ICA) blindly separates mixed recorded signals and is employed in various engineering disciplines like speech, biomedical, communication, robotics, leakage detection, vibration analysis and machinery fault diagnosis. Order and phase indeterminacies exist in the ICA post processed signals. These indeterminacies appear due to the use of non-linearities in the development of the ICA algorithms which also restrict the practical applications of ICA. This issue of indeterminacies is not very well explored in the research. In the literature, the signals order ambiguity is resolved using various criterion that can order the signals in ascending or descending order based on a defined criteria. Although, in practice, it is required that the separated signals should be in the order of the mixing source signals. Moreover, the phase correction performed in the literature is through complicated signal processing techniques. In this paper, we propose an efficient and more rational signals ordering and phase correction technique. The proposed technique orders the separated signals in the order of the mixing source signals that makes the proposed technique more practical. Further, we have analysed the resultant mixing and un-mixing matrices, to overcome the phase and order ambiguities in the ICA post processed signals. Audio signals have been utilized for simulation purpose. Furthermore, we have utilize the fastica algorithm to observe the effectiveness of the proposed signals ordering and phase correction technique (SOPCT). The simple mathematical formulation and efficient simulation performance make the SOPCT technique, a best choice for the practical scenarios.

关键词： Independent component analysis Phase correction Order ambiguity fastica algorithm

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Blind Information Extraction of Machine Faults Based on Separating Matrix 5

Blind Information Extraction of Machine Faults Based on Sepa...

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IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Li, Hao Tan, Yifan Pu, Yun SWJTU Sch Transportat & Logist Chengdu Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu Peoples R China Officers Coll PAP Chengdu Peoples R China

ISBN: (纸本)9781728194097

Blind signal processing is an effective feature extraction method for mechanical vibration signals. However due to noise corruption, independent source signals can't always be accurately recovered or separated from the acquired sensor observations. Then feature information extracted from source signals can't naturally represent machine states of the detected mechanical equipment. Generally in blind signal processing, the separating matrix may contain as much information contents as the separated source signals. The separating matrix can directly be processed to extract its singular values as useful feature information by the singular value decomposition (SVD) method. Thus a blind information extraction method was proposed to extract singular values of separating matrix as the desired feature information of the detected machine. The experimental results of gear pump indicate that this method can be applied to feature extraction of mechanical equipment.

关键词： Blind source separation Independent component analysis fastica algorithm Separating matrix Singular value decomposition Feature extraction

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Traffic Characteristics Analyses of Intersections Based on the Road Detector Data 19

Traffic Characteristics Analyses of Intersections Based on t...

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19th COTA International Conference of Transportation Professionals (CICTP) - Transportation in China 2025

作者： Xue, Yunqiang Zhao, Yuanyang Cheng, Lin An, Jing Xue, Luowei Sang, Zi East China Jiaotong Univ Coll Transportat & Logist Nanchang 330013 Jiangxi Peoples R China Southeast China Univ Sch Transportat Nanjing 211102 Peoples R China

ISBN: (数字)9780784482292

ISBN: (纸本)9780784482292

Intersection traffic characteristics show a complex nonlinear and superposition phenomena of different time scales. In order to find the potential intersection characteristics, this paper combines spectral clustering algorithm and fastica algorithm to study the intersection traffic characteristics at different lengths of time (hours, days, and weeks) based on road detector data. This method can present the slight difference of the traffic flow variation in each direction of the intersection, and get more detailed results. The research provides a theoretical support for comprehensive evaluation of intersections and the following studies.

关键词： Road detector data Time length Spectral clustering fastica algorithm

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Blind Separation of Weak Object Signals Against the Unknown Strong Jamming in Communication Systems

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WIRELESS PERSONAL COMMUNICATIONS 2017年第3期97卷 4265-4283页

作者： Li, Chengjie Zhu, Lidong Xie, Anhong Luo, Zhongqiang Southwest Minzu Univ Sch Comp Sci & Technol Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Zigong Peoples R China

To obtain the mixed weak object signal against the super power signal (jamming) is still an challenging task in modern communication systems. In this paper, a novel framework is designed for weak object signal blind separation against the strong interference signal. To extract the strong interference signal,firstly, we separate the mixed signals with the optimized fastica algorithm, then, an improved Interference Cancellation algorithm is proposed as reference signal based on the separated strong signal. Next, we separate the weak mixed signals by the improved fastica algorithm again. Finally, we discuss the performance of the proposed method and verify the novel method based on several simulations. The experimental results demonstrate the effectiveness and robustness of the proposed method.

关键词： Blind signal separation Gauss channel K-means clustering fastica algorithm

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Cost-Effective and Variable-Channel fastica Hardware Architecture and Implementation for EEG Signal Processing

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2016年第1期82卷 91-113页

作者： Van, Lan-Da Huang, Po-Yen Lu, Tsung-Che Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan

This paper proposes a cost-effective and variable-channel floating-point fast independent component analysis (fastica) hardware architecture and implementation for EEG signal processing. The Gram-Schmidt orthonormalization based whitening process is utilized to eliminate the use of the dedicated hardware for eigenvalue decomposition (EVD) in the fastica algorithm. The proposed two processing units, PU1 and PU2, in the presented fastica hardware architecture can be reused for the centering operation of preprocessing and the updating step of the fixed-point algorithm of the fastica algorithm, and PU1 is reused for Gram-Schmidt orthonormalization operation of preprocessing and fixed-point algorithm to reduce the hardware cost and support 2-to-16 channel fastica. Apart from the fastica processing, the proposed hardware architecture supports re-reference, synchronized average, and moving average functions. The cost-effective and variable-channel fastica hardware architecture is implemented in 90 nm 1P9M complementary metal-oxide-semiconductor (CMOS) process. As a result, the fastica hardware implementation consumes 19.4 mW at 100 MHz with a 1.0 V supply voltage. The core size of the chip is 1.43 mm(2). From the experimental results, the presented work achieves satisfactory performance for each function.

关键词： Cost effective EEG signal processing fastica algorithm Gram-Schmidt Hardware architecture Variable channel

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MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

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BMC BIOINFORMATICS 2016年第1期17卷 1-14页

作者： Liu, Youzhong Smirnov, Kirill Lucio, Marianna Gougeon, Regis D. Alexandre, Herve Schmitt-Kopplin, Philippe Helmholtz Zentrum Munchen Dept Environm Sci Res Unit Analyt BioGeoChem Ingolstadter Landstr 1 D-85758 Neuherberg Germany UMR PAM Univ Bourgogne Inst Univ Vigne & Vin Agrosup Dijon Rue Claude LadreyBP 27877 Dijon France Tech Univ Munich Chair Analyt Food Chem Alte Akad 1085354 Freising Weihenstephan Germany

Background: Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve. Results: ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained. Conclusions: Correlating the components obtained with prior biological knowledge allows understanding how nontargeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://***/daniellyz/MetICA.

关键词： Independent Component Analysis Independent Component Analysis algorithm algorithm Input Independent Component Analysis Method fastica algorithm

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Blind Signal Separation in Distributed Space-Time Coding Systems Using the fastica algorithm

Blind Signal Separation in Distributed Space-Time Coding Sys...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fan, Xianxue Igual, Jorge Llinares, Raul Salazar, Addisson Wu, Gang Department of Communications Universitat Politecnica Valencia Valencia Camino de Vera sin Spain 28th Research Institute of China Electronics Technology Group Corporation China National Key Lab of Communications University of Electronic Science and Technology of China Chengdu Sichuan 611731 China

ISBN: (纸本)9781424496365

One of the main advantages of cooperative communication systems is the use of information at the surrounding nodes in order to create spatial diversity and so far obtaining higher throughput and reliability. We propose in this paper a blind detector that involves the formulation of the system as a Blind Source Separation BSS problem. In the BSS framework, we do not have to estimate the channel using training data, removing the necessity of pilot symbols and the prior estimation of the channel. We analyze two kinds of distributed space-time codes for the single relay system, showing that they can be stated in terms of BSS as a linear instantaneous mixture of complex-valued sources. The BSS method applied is the complex version of the fastica algorithm since it is very flexible, robust and the convergence is very fast so we can estimate the symbols accurately with a low-complexity algorithm that can adapt to changes in the channel with relative simplicity.

关键词： BSS problem Matrix decomposition Protocols Relays Robustness blind detector blind signal separation problem blind source separation channel estimation complex-valued source cooperative communication cooperative communication system distributed space-time coding system fastica algorithm independent component analysis linear instantaneous mixture low-complexity algorithm pilot symbol relays single relay system space-time codes spatial diversity telecommunication network reliability

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