The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and...The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.展开更多
Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed b...Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.展开更多
A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic inf...A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic information is dynamically specified for each component decoder.The simulation results show that the iterative decoder with the new technique outperforms that of the decoder with the traditional Gaussian approach for the extrinsic information under the same conditions.展开更多
The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. <span style="font-family:Verdana;">Test number ...The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. <span style="font-family:Verdana;">Test number six in the NIST document is the Discrete Fourier Transform</span><span style="font-family:Verdana;"> (DFT) test suitable for stationary incoming sequences. But, for cases where the input sequence is not stationary, the DFT test provides inaccurate results. For these cases, test number seven and eight (the Non-overlapping Template Matching Test and the Overlapping Template Matching Test) of the NIST document were designed to classify those non-stationary sequences. But, even with test number seven and eight of the NIST document, the results are not always accurate. Thus, the NIST test does not give a proper answer for the non-stationary input sequence case. In this paper, we offer a new algorithm </span><span style="font-family:Verdana;">or test, which may replace the NIST tests number six, seven and eight. The</span> <span style="font-family:Verdana;">proposed test is applicable also for non-stationary sequences and supplies</span><span style="font-family:Verdana;"> more </span><span style="font-family:Verdana;">accurate results than the existing tests (NIST tests number six, seven and</span><span style="font-family:Verdana;"> eight), for non-stationary sequences. The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD). In addition, </span><span style="font-family:Verdana;">this new proposed algorithm alarms and indicates on suspicious places of</span><span style="font-family:Verdana;"> cyclic </span><span style="font-family:Verdana;">sections in the tested sequence. Thus, it gives us the option to repair or to</span><span style="font-family:Verdana;"> remove the suspicious places of cyclic sections</span><span><span><span><span></span><span></span><b><span style="font-family:;" "=""><span></span><span></span> </span></b></span></span></span><span><span><span><span></span><span></span><span style="font-family:;" "=""><span></span><span></span><span style="font-family:Verdana;">(this part is beyond the scope </span><span style="font-family:Verdana;">of this paper), so that after that, the repaired or the shortened sequence</span><span style="font-family:Verdana;"> (origi</span><span style="font-family:Verdana;">nal sequence with removed sections) will result as a sequence with high</span><span style="font-family:Verdana;"> probability of random degree.</span></span></span></span></span>展开更多
Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy.In this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distr...Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy.In this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distributions,is applied to formulate the non-Gaussian forecasting-aided state estimation problem.To address the problem,an improved particle filter is proposed,which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle filter.The AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation,through considering both state transitions and latest measurements.For rapid particle generation from the AGGD proposal function,an efficient inverse cumulative distribution function(CDF)sampling method is employed based on the derived approximation of inverse CDF of AGGD.Numerical simulations are carried out on a modified balanced IEEE 123-bus test system.The results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness,whether in Gaussian,non-Gaussian,or abnormal measurement errors.展开更多
A new speckle suppression method in contourlet domain was presented. By modeling the subband contourlet coefficients of the ultrasound images after logarithmic transform as generalized Gaussian distribution (GGD), we ...A new speckle suppression method in contourlet domain was presented. By modeling the subband contourlet coefficients of the ultrasound images after logarithmic transform as generalized Gaussian distribution (GGD), we gave a scale-adaptive threshold in Bayesian framework. Experimental results of both synthetic and clinical ultrasound images show that our method has a better performance on speckle suppressing than the wavelet-based method while well preserving the feature details.展开更多
Adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of o...Adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution(GGD)but with different parameters(i.e.,shape factor,mean,and variance).GGD is a general distribution family that covers many popular distributions(e.g.,Laplacian,Gaussian,or uniform).Therefore,it is more likely to approximate the intrinsic distributions of internal responses than any specific distribution.Besides,since the shape factor is more robust to different databases rather than the other two parameters,we propose to construct discriminative features via the shape factor for adversarial detection,employing the magnitude of Benford-Fourier(MBF)coefficients,which can be easily estimated using responses.Finally,a support vector machine is trained as an adversarial detector leveraging the MBF features.Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.展开更多
We designed and implemented a signal generator that can simulate the output of the NaI（Tl）/CsI（Na）detectors＇pre-amplifier onboard the Hard X-ray Modulation Telescope（HXMT）.Using the development of the FPGA（Fie...We designed and implemented a signal generator that can simulate the output of the NaI（Tl）/CsI（Na）detectors＇pre-amplifier onboard the Hard X-ray Modulation Telescope（HXMT）.Using the development of the FPGA（Field Programmable Gate Array）with VHDL language and adding a random constituent,we have finally produced the double exponential random pulse signal generator.The statistical distribution of the signal amplitude is programmable.The occurrence time intervals of the adjacent signals contain negative exponential distribution statistically.展开更多
基金Sponsored by the National Nature Science Foundation of China(11074308)China Postdoctoral Science Foundation(201003754)
文摘The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.
基金Supported by the National Natural Science Foundation of China(No.51204145)Natural Science Foundation of Hebei Province of China(No.2013203300)
文摘Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.
基金Supported by the National Aeronautical Foundation of Science and Research of China (No.00F52048)
文摘A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic information is dynamically specified for each component decoder.The simulation results show that the iterative decoder with the new technique outperforms that of the decoder with the traditional Gaussian approach for the extrinsic information under the same conditions.
文摘The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. <span style="font-family:Verdana;">Test number six in the NIST document is the Discrete Fourier Transform</span><span style="font-family:Verdana;"> (DFT) test suitable for stationary incoming sequences. But, for cases where the input sequence is not stationary, the DFT test provides inaccurate results. For these cases, test number seven and eight (the Non-overlapping Template Matching Test and the Overlapping Template Matching Test) of the NIST document were designed to classify those non-stationary sequences. But, even with test number seven and eight of the NIST document, the results are not always accurate. Thus, the NIST test does not give a proper answer for the non-stationary input sequence case. In this paper, we offer a new algorithm </span><span style="font-family:Verdana;">or test, which may replace the NIST tests number six, seven and eight. The</span> <span style="font-family:Verdana;">proposed test is applicable also for non-stationary sequences and supplies</span><span style="font-family:Verdana;"> more </span><span style="font-family:Verdana;">accurate results than the existing tests (NIST tests number six, seven and</span><span style="font-family:Verdana;"> eight), for non-stationary sequences. The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD). In addition, </span><span style="font-family:Verdana;">this new proposed algorithm alarms and indicates on suspicious places of</span><span style="font-family:Verdana;"> cyclic </span><span style="font-family:Verdana;">sections in the tested sequence. Thus, it gives us the option to repair or to</span><span style="font-family:Verdana;"> remove the suspicious places of cyclic sections</span><span><span><span><span></span><span></span><b><span style="font-family:;" "=""><span></span><span></span> </span></b></span></span></span><span><span><span><span></span><span></span><span style="font-family:;" "=""><span></span><span></span><span style="font-family:Verdana;">(this part is beyond the scope </span><span style="font-family:Verdana;">of this paper), so that after that, the repaired or the shortened sequence</span><span style="font-family:Verdana;"> (origi</span><span style="font-family:Verdana;">nal sequence with removed sections) will result as a sequence with high</span><span style="font-family:Verdana;"> probability of random degree.</span></span></span></span></span>
基金supported by the National Key Research and Development Program of China(No.2016YFB0900100)the Key Project of Shanghai Science and Technology Committee(No.18DZ1100303)。
文摘Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy.In this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distributions,is applied to formulate the non-Gaussian forecasting-aided state estimation problem.To address the problem,an improved particle filter is proposed,which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle filter.The AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation,through considering both state transitions and latest measurements.For rapid particle generation from the AGGD proposal function,an efficient inverse cumulative distribution function(CDF)sampling method is employed based on the derived approximation of inverse CDF of AGGD.Numerical simulations are carried out on a modified balanced IEEE 123-bus test system.The results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness,whether in Gaussian,non-Gaussian,or abnormal measurement errors.
基金the National Basic Research Program(973) of China (No. 2003CB716103)
文摘A new speckle suppression method in contourlet domain was presented. By modeling the subband contourlet coefficients of the ultrasound images after logarithmic transform as generalized Gaussian distribution (GGD), we gave a scale-adaptive threshold in Bayesian framework. Experimental results of both synthetic and clinical ultrasound images show that our method has a better performance on speckle suppressing than the wavelet-based method while well preserving the feature details.
基金supported by Natural Science Foundation of China(No.62076213)Shenzhen Science and Technology Program,China(No.RCYX20210609103057050)+1 种基金the university development fund of The Chinese University of Hong Kong,Shenzhen,China(No.01001810)Guangdong Provincial Key Laboratory of Big Data Computing,The Chinese University of Hong Kong,Shenzhen,China.
文摘Adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution(GGD)but with different parameters(i.e.,shape factor,mean,and variance).GGD is a general distribution family that covers many popular distributions(e.g.,Laplacian,Gaussian,or uniform).Therefore,it is more likely to approximate the intrinsic distributions of internal responses than any specific distribution.Besides,since the shape factor is more robust to different databases rather than the other two parameters,we propose to construct discriminative features via the shape factor for adversarial detection,employing the magnitude of Benford-Fourier(MBF)coefficients,which can be easily estimated using responses.Finally,a support vector machine is trained as an adversarial detector leveraging the MBF features.Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.
基金Supported by the 973 Program(2009CB824800),NSFC(10978001,11003011)the Knowledge Innovation Program of Chinese Academy of Sciences(200931111192010)
文摘We designed and implemented a signal generator that can simulate the output of the NaI（Tl）/CsI（Na）detectors＇pre-amplifier onboard the Hard X-ray Modulation Telescope（HXMT）.Using the development of the FPGA（Field Programmable Gate Array）with VHDL language and adding a random constituent,we have finally produced the double exponential random pulse signal generator.The statistical distribution of the signal amplitude is programmable.The occurrence time intervals of the adjacent signals contain negative exponential distribution statistically.
