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
Most remote systems require user authentication to access resources.Text-based passwords are still widely used as a standard method of user authentication.Although conventional text-based passwords are rather hard to ...Most remote systems require user authentication to access resources.Text-based passwords are still widely used as a standard method of user authentication.Although conventional text-based passwords are rather hard to remember,users often write their passwords down in order to compromise security.One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users.Graphical user authentication methods have recently been proposed to verify the user identity.However,the fundamental limitation of a graphi-cal password is that it must have a colorful and rich image to provide an adequate password space to maintain security,and when the user clicks and inputs a pass-word between two possible grids,the fault tolerance is adjusted to avoid this situation.This paper proposes an enhanced graphical authentication scheme,which comprises benefits over both recognition and recall-based graphical techniques besides image steganography.The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts.To evaluate the effectiveness of the proposed scheme,peak signal-to-noise ratio and mean squared error parameters have been used.展开更多
The signal processing speed of spectral domain optical coherence tomography(SD-OCT)has become a bottleneck in a lot of medical applications.Recently,a time-domain interpolation method was proposed.This method can get ...The signal processing speed of spectral domain optical coherence tomography(SD-OCT)has become a bottleneck in a lot of medical applications.Recently,a time-domain interpolation method was proposed.This method can get better signal-to-noise ratio(SNR)but much-reduced signal processing time in SD-OCT data processing as compared with the commonly used zeropadding interpolation method.Additionally,the resampled data can be obtained by a few data and coefficients in the cutoff window.Thus,a lot of interpolations can be performed simultaneously.So,this interpolation method is suitable for parallel computing.By using graphics processing unit(GPU)and the compute unified device architecture(CUDA)program model,time-domain interpolation can be accelerated significantly.The computing capability can be achieved more than 250,000 A-lines,200,000 A-lines,and 160,000 A-lines in a second for 2,048 pixel OCT when the cutoff length is L=11,L=21,and L=31,respectively.A frame SD-OCT data(400A-lines×2,048 pixel per line)is acquired and processed on GPU in real time.The results show that signal processing time of SD-OCT can befinished in 6.223 ms when the cutoff length L=21,which is much faster than that on central processing unit(CPU).Real-time signal processing of acquired data can be realized.展开更多
干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问...干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问题转化为时频谱图中的多目标检测问题,进而利用改进的目标检测算法进行识别。实验结果表明,该方法能有效识别传输频带内音调干扰、噪声干扰、扫频干扰、脉冲噪声干扰和锯齿波扫频干扰的类型、数量、干扰频率和时间范围,同时相比改进前的YOLOv3算法,平均精度提高了7.6%,权值文件、参数量和计算量分别降低了82.5%,82.6%,90%,对能耗受限场景下的实时检测具有潜在应用价值。展开更多
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.
基金The researcher would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Most remote systems require user authentication to access resources.Text-based passwords are still widely used as a standard method of user authentication.Although conventional text-based passwords are rather hard to remember,users often write their passwords down in order to compromise security.One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users.Graphical user authentication methods have recently been proposed to verify the user identity.However,the fundamental limitation of a graphi-cal password is that it must have a colorful and rich image to provide an adequate password space to maintain security,and when the user clicks and inputs a pass-word between two possible grids,the fault tolerance is adjusted to avoid this situation.This paper proposes an enhanced graphical authentication scheme,which comprises benefits over both recognition and recall-based graphical techniques besides image steganography.The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts.To evaluate the effectiveness of the proposed scheme,peak signal-to-noise ratio and mean squared error parameters have been used.
基金supported by National High Technology R&D project of China(2008AA02Z422)The Instrument Developing Project of The Chinese Academy of Sciences,Institute of Optics and Electronic,Chinese Academy of Sciences.
文摘The signal processing speed of spectral domain optical coherence tomography(SD-OCT)has become a bottleneck in a lot of medical applications.Recently,a time-domain interpolation method was proposed.This method can get better signal-to-noise ratio(SNR)but much-reduced signal processing time in SD-OCT data processing as compared with the commonly used zeropadding interpolation method.Additionally,the resampled data can be obtained by a few data and coefficients in the cutoff window.Thus,a lot of interpolations can be performed simultaneously.So,this interpolation method is suitable for parallel computing.By using graphics processing unit(GPU)and the compute unified device architecture(CUDA)program model,time-domain interpolation can be accelerated significantly.The computing capability can be achieved more than 250,000 A-lines,200,000 A-lines,and 160,000 A-lines in a second for 2,048 pixel OCT when the cutoff length is L=11,L=21,and L=31,respectively.A frame SD-OCT data(400A-lines×2,048 pixel per line)is acquired and processed on GPU in real time.The results show that signal processing time of SD-OCT can befinished in 6.223 ms when the cutoff length L=21,which is much faster than that on central processing unit(CPU).Real-time signal processing of acquired data can be realized.
文摘干扰识别是无线电监测和通信抗干扰的关键环节。针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)宽带传输系统中潜在的干扰问题,提出了一种基于目标检测网络的干扰识别方法。核心思想是将传输频带中的多干扰识别问题转化为时频谱图中的多目标检测问题,进而利用改进的目标检测算法进行识别。实验结果表明,该方法能有效识别传输频带内音调干扰、噪声干扰、扫频干扰、脉冲噪声干扰和锯齿波扫频干扰的类型、数量、干扰频率和时间范围,同时相比改进前的YOLOv3算法,平均精度提高了7.6%,权值文件、参数量和计算量分别降低了82.5%,82.6%,90%,对能耗受限场景下的实时检测具有潜在应用价值。
基金Projects(52172326,61773168)supported by the National Natural Science Foundation of ChinaProject(2023A1515012815)supported by the Basic and Applied Basic Research Foundation of Guangdong Province,ChinaProject(21KJB580016)supported by the Natural Science Foundation of the Higher Education Institutions in Jiangsu Province,China。
