Statistics Modeling of Shallow Sea Ambient Noise and Its Applications in Low-frequency Line Spectrum Detection

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.

A New Approach for the DFT NIST Test Applicable for Non-Stationary Input Sequences

The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree. Test number six in the NIST document is the Discrete Fourier Transform (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 or test, which may replace the NIST tests number six, seven and eight. The proposed test is applicable also for non-stationary sequences and supplies more accurate results than the existing tests (NIST tests number six, seven and eight), for non-stationary sequences. The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD). In addition, this new proposed algorithm alarms and indicates on suspicious places of cyclic sections in the tested sequence. Thus, it gives us the option to repair or to remove the suspicious places of cyclic sections (this part is beyond the scope of this paper), so that after that, the repaired or the shortened sequence (original sequence with removed sections) will result as a sequence with high probability of random degree.

Improved Particle Filter for Non-Gaussian Forecasting-aided State Estimation

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.

Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

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.

Design and implementation of the NaI(Tl)/CsI(Na) detectors output signal generator

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.