In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the ...In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.展开更多
The sense of being within a three-dimensional （3D） space and interacting with virtual 3D objects in a computer-generated virtual environment （VE） often requires essential image, vision and sensor signal processing...The sense of being within a three-dimensional （3D） space and interacting with virtual 3D objects in a computer-generated virtual environment （VE） often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit （GPU）. While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence （AI） and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.展开更多
In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simulta...In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.展开更多
文摘In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.
基金supported by Research Funding of Huddersfield University:GPU-based High Performance Computing for Signal Processing (No. 1008/REU117)
文摘The sense of being within a three-dimensional （3D） space and interacting with virtual 3D objects in a computer-generated virtual environment （VE） often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit （GPU）. While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence （AI） and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.
文摘In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.
