Noise Circumstances Research Articles

A Novel Multiple-Bits Collision Attack Based on Double Detection with Error-Tolerant Mechanism

Side-channel collision attacks are more powerful than traditional side-channel attack without knowing the leakage model or establishing the model. Most attack strategies proposed previously need quantities of power traces with high computational complexity and are sensitive to mistakes, which restricts the attack efficiency seriously. In this paper, we propose a multiple-bits side-channel collision attack based on double distance voting detection (DDVD) and also an improved version, involving the error-tolerant mechanism, which can find all 120 relations among 16 key bytes when applied to AES (Advanced Encryption Standard) algorithm. In addition, we compare our collision detection method called DDVD with the Euclidean distance and the correlation-enhanced collision method under different intensity of noise, which indicates that our detection technique performs better in the circumstances of noise. Furthermore, 4-bit model of our collision detection method is proven to be optimal in theory and in practice. Meanwhile the corresponding practical attack experiments are also performed on a hardware implementation of AES-128 on FPGA board successfully. Results show that our strategy needs less computation time but more traces than LDPC method and the online time for our strategy is about 90% less than CECA and 96% less than BCA with 90% success rate.

Magnetoelectric coupling of a magnetoelectric flux gate sensor in vibration noise circumstance

A magnetoelectric (ME) flux gate sensor (MEFGS) consisting of piezoelectric PMN-PT single crystals and ferromagnetic amorphous alloy ribbon in a self-differential configuration is featured with the ability of weak magnetic anomaly detection. Here, we further investigated its ME coupling and magnetic field detection performance in vibration noise circumstance, including constant frequency, impact, and random vibration noise. Experimental results show that the ME coupling coefficient of MEFGS is as high as 5700 V/cm*Oe at resonant frequency, which is several orders magnitude higher than previously reported differential ME sensors. It was also found that under constant and impact vibration noise circumstance, the noise reduction and attenuation factor of MEFGS are over 17 and 85.7%, respectively. This work is important for practical application of MEFGS in real environment.

Effects of Second-Order Matched Stochastic Resonance for Weak Signal Detection

Weak signal detection via stochastic resonance (SR) has attracted considerable attention in a wide range of research fields, especially under heavy background noise circumstances. In this paper, a second-order matched stochastic resonance (SMSR) method is proposed to further improve the signal-to-noise ratio of weak period signal. By selecting a proper damping factor in the regime of second-order parameter matched relationship, weak periodic signal, background noise, and nonlinear system can be matched in generating an enhanced output. The matched relationship is deduced in combining noise intensity optimization and signal frequency synchronization with duffing system in a mathematical way, and a normalized scale transformation is further carried out to make it accessible in detecting arbitrary high frequency signals. The numerical analysis and application verification are performed to confirm the validity and effectiveness of theoretical results, which indicate the proposed SMSR method is superior to the first-order parameter matched stochastic resonance in achieving a good band-pass filtering effect with a low-noise output as the driving frequency of received signal is not too small ( $\geqslant 0.1$ Hz). Thus, the proposed method is beneficial to practical engineering weak signal processing and anticipates to be a potential novel technique for ship radiated line-spectrum detection.

Robust measure of non‐linearity‐based cubature Kalman filter

In this study, a novel robust measure of non-linearity-based cubature Kalman filter (RMoNCKF) is proposed to obtain good performance with lower computational burden. The proposed filter inherits the virtues of high accuracy of the high-degree filter and computation efficiency of the low-degree one. When the measure of non-linearity (MoN) is evaluated and compared with the threshold in the dynamic system, the cubature rules nested in the RMoNCKF can be switched autonomously to decrease the computation complexity in the low non-linear condition. Furthermore, the robust estimation technology can help to improve the value of MoN for the non-Gaussian distributed case. Simulation results of target tracking and integrated navigation system demonstrate that the RMoNCKF can have a close performance to the fifth-degree CKF with less computation time. In the circumstances of the time-varying noise and contaminated Gaussian distributed noise, the RMoNCKF outperforms the UKF, the third-degree CKF and fifth-degree CKF.

Non-linear classifiers applied to EEG analysis for epilepsy seizure detection

Abstract This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions.

A Secure and Efficient Scalable Secret Image Sharing Scheme with Flexible Shadow Sizes.

In a general (k, n) scalable secret image sharing (SSIS) scheme, the secret image is shared by n participants and any k or more than k participants have the ability to reconstruct it. The scalability means that the amount of information in the reconstructed image scales in proportion to the number of the participants. In most existing SSIS schemes, the size of each image shadow is relatively large and the dealer does not has a flexible control strategy to adjust it to meet the demand of differen applications. Besides, almost all existing SSIS schemes are not applicable under noise circumstances. To address these deficiencies, in this paper we present a novel SSIS scheme based on a brand-new technique, called compressed sensing, which has been widely used in many fields such as image processing, wireless communication and medical imaging. Our scheme has the property of flexibility, which means that the dealer can achieve a compromise between the size of each shadow and the quality of the reconstructed image. In addition, our scheme has many other advantages, including smooth scalability, noise-resilient capability, and high security. The experimental results and the comparison with similar works demonstrate the feasibility and superiority of our scheme.

Selection-based detectors and fusion centers for cooperative cognitive radio networks in heavy-tailed noise environment

In this paper, nonlinear schemes are proposed and analyzed for the spectrum sensing in cooperative cognitive radio networks under the influence of impulsive (heavy-tailed) noise. By jointly employing the order statistics, generalized likelihood ratio test, and counting rule in the framework of spectrum sensing according to the noise environment, the proposed scheme is shown to exhibit a better performance than the conventional counterparts. Through computer simulations, the performance characteristics of the proposed cooperative spectrum sensing scheme are investigated and analyzed in various noise circumstances. It is confirmed from numerical simulation results that the proposed scheme, under various noise circumstances which might be different from one cognitive radio to another, can provide significant improvements of performance over the conventional schemes.

Effects of Compound K-Distributed Sea Clutter on Angle Measurement of Wideband Monopulse Radar

The effects of compound K-distributed sea clutter on angle measurement of wideband monopulse radar are investigated in this paper. We apply the conditional probability density function (pdf) of monopulse ratio (MR) error to analyze these effects. Based on the angle measurement procedure of the wideband monopulse radar, this conditional pdf is first deduced in detail for the case of compound K-distributed sea clutter plus noise. Herein, the spatial correlation of the texture components for each channel clutter and the correlation of the texture components between the sum and difference channel clutters are considered, and two extreme situations for each of them are tackled. Referring to the measured sea clutter data, angle measurement performances in various K-distributed sea clutter plus noise circumstances are simulated, and the effects of compound K-distributed sea clutter on angle measurement are discussed.

Complex Nonlinear Dynamic System of Oligopolies Price Game with Heterogeneous Players Under Noise

A nonlinear four oligopolies price game with heterogeneous players, that are boundedly rational and adaptive, is built using two different special demand costs. Based on the theory of complex discrete dynamical system, the stability and the existing equilibrium point are investigated. The complex dynamic behavior is presented via bifurcation diagrams, the Lyapunov exponents to show equilibrium state, bifurcation and chaos with the variation in parameters. As disturbance is ubiquitous in economic systems, this paper focuses on the analysis of delay feedback control method under noise circumstances. Stable dynamics is confirmed to depend mainly on the low price adjustment speed, and if all four players have limited opportunities to stabilize the market, the new adaptive player facing profits of scale are found to be higher than the incumbents of bounded rational.

The ROI CT problem: a shearlet-based regularization approach

The possibility to significantly reduce the X-ray radiation dose and shorten the scanning time is particularly appealing, especially for the medical imaging community. Region- of-interest Computed Tomography (ROI CT) has this potential and, for this reason, is currently receiving increasing attention. Due to the truncation of projection images, ROI CT is a rather challenging problem. Indeed, the ROI reconstruction problem is severely ill-posed in general and naive local reconstruction algorithms tend to be very unstable. To obtain a stable and reliable reconstruction, under suitable noise circumstances, we formulate the ROI CT problem as a convex optimization problem with a regularization term based on shearlets, and possibly nonsmooth. For the solution, we propose and analyze an iterative approach based on the variable metric inexact line-search algorithm (VMILA). The reconstruction performance of VMILA is compared against different regularization conditions, in the case of fan-beam CT simulated data. The numerical tests show that our approach is insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

Research on a practical de-noising method and the characterization of partial discharge UHF signals

Experimental studies on partial discharges (PD) occurred in oil-insulated devices using ultra-high frequency (UHF) sensors to detect PD signals and using wavelet analysis to process the acquired UHF signals are presented, and a threshold selecting method adapting to various noise circumstances when de-noising UHF signals using wavelet transform is proposed in this paper. The threshold method was based on numerical fitting of standard deviations and manual setting thresholds for brush-fire samples from a large scale. Horizontal comparisons between different geometries and vertical comparisons between different intensities of partial discharges in oil were also discussed and the mathematical tool was wavelet analysis. Multi-resolution analysis (MRA) results show the significant differences that can be used to recognize the discharge pattern and to judge the intensity. Particularly attention was given to needle-plate discharge geometry, which was often used by researchers focusing on the mechanism of pre-breakdown phenomena in liquid dielectrics. The results of needle-plate model show good agreement with electronic theory of liquid pre-breakdown mechanism.

Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T2 mapping

T2 mapping is a powerful noninvasive technique providing quantitative biological information of the inherent tissue properties. However, its clinical usage is limited due to the relative long scanning time. This paper proposed a novel model-based method to address this problem. Typically, we directly estimated the relaxation values from undersampled k-space data by exploiting the sparse property of proton density and T2 map in a penalized maximum likelihood formulation. An alternating minimization approach was presented to estimate the relaxation maps separately. Both numerical phantom and in vivo experiment dataset were used to demonstrate the performance of the proposed method. We showed that the proposed method outperformed the state-of-the-art techniques in terms of detail preservation and artifact suppression with various reduction factors and in both moderate and heavy noise circumstances. The superior reconstruction performance validated its promising potential in fast T2 mapping applications.

Multi component LFM signal detection and parameter estimation based on EEMD–FRFT

This paper studied multi component LFM signal detection and parameter estimation under the noise circumstance of various signal-to-noise ratios. Based on the analysis of fractional Fourier transform detection and parameter estimation on simple component LFM signal, this paper proposed the method of multi component LFM signal detection and parameter estimation based on EEMD–FRFT (Ensemble Empirical Mode Decomposition–Fractional Fourier transform), and this method was that with the EEMD algorithm, from the frequency domain decompose the analyzable signal to narrow-bandwidth components, whose center frequency changed from high to low, then accurately estimate the parameter and detect the signal of each component out of the pseudo-component with FrFT. This method solved the problem of mode aliasing of signal decomposition; meanwhile, the problem of detecting the multi component LFM signal would be simplified as the problem of one-dimensional search in small scope, which could reduce the amount of operation and improved the detection accuracy. A simulation computation for multi component LFM signal of various SNR (signal-to-noise ratios) was made and the result showed that the error of parameter estimation was less than 5% in the case of SNR not less than −10dB.

Support vector regression via MCMC within evidence framework

This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR’s weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.

Two-stage refined instrumental variable method for identifying Hammerstein-Wiener continuous-time models in closed loop

The continuous-time model identification problem of closed-loop Hammerstein-Wiener system with unknown controller and different noise situations is studied. A two-stage approach is proposed, based on the refined instrumental variable method. With the assumption of monotonic nonlinear function, the closed-loop non linear model is iteratively estimated as an over-parameterized MISO LTI model. To obtain an accurate model in the closed-loop case, the identification is implemented in two stages: 1) identification between the input and the reference signal, which produces estimate of the noise-free input, 2) identification between the output and estimated noise-free input signal. Monte Carlo simulation analysis is carried out to illustrate the effectiveness of the proposed method, in both output and process noise circumstances.

A Noise-Robust Continuous Speech Recognition System Using Block-Based Dynamic Range Adjustment

A new approach to speech feature estimation under noise circumstances is proposed in this paper. It is used in noise-robust continuous speech recognition (CSR). As the noise robust techniques in isolated word speech recognition, the running spectrum analysis (RSA), the running spectrum filtering (RSF) and the dynamic range adjustment (DRA) methods have been developed. Among them, only RSA has been applied to a CSR system. This paper proposes an extended DRA for a noise-robust CSR system. In the stage of speech recognition, a continuous speech waveform is automatically assigned to a block defined by a short time length. The extended DRA is applied to these estimated blocks. The average recognition rate of the proposed method has been improved under several different noise conditions. As a result, the recognition rates are improved up to 15% in various noises with 10 dB SNR.

Sound Category Detection Including the Human Speech like Noise after Independent Component Analysis and the Sound Trace

For separating the mixed sound into the each separated sounds including the noise removing, ICA (Independent Component Analysis) is frequently used, and the Kalman Filter is used for the sound trace and then elimination of the sound itself is done. But even after the separation of the sound by ICA we cannot yet say which category the said sound belong to or even if the specified sound is traced by Kalman Filter, whether its sound is the target which should be taken out as the pure one or ot cannot be known. So the classification of the said sound is required as the post-processing, especially judging for HSLN (Human Speech Like Noise) which means the actual voice and the others. Limiting its sound pressure level of HSLN into 50dB and that of the other sound source into 30dB as the actual noise circumstances,the specification of the separation by ICA could be done successfully by 78.5%. By this trial Speech recognition ratio was improved from 55% to 63% averagely.

잡음 환경에서의 유도 전동기 고장 검출 및 분류를 위한 강인한 특징 벡터 추출에 관한 연구

유도 전동기는 항공 산업, 자동차 산업 등의 산업 현장에서 중요한 역할을 하고 있으며, 이러한 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이에 본 논문에서는 정상 및 각종 비정상 상태의 유도 전동기 진동 신호에 대해 부분 자기 상관(partial autocorrelation, PARCOR) 계수, 로그 스펙트럼 파워(log spectrum powers, LSP), 캡스트럼 계수의 평균값(cepstrum coefficients mean, CCM), 멜 주파수 캡스트럼 계수(mel-frequency cepstrum coefficient, MFCC)의 네 가지 특징 벡터를 신경 회로망의 입력으로 사용하여 유도 전동기의 고장을 검출하고 분류하였다. 고장 분류를 위한 최적의 특징 벡터를 찾기 위해 추출하는 특징의 수를 2에서 20으로 바꾸어 가며 분류 성능을 평가한 결과 CCM을 제외한 나머지의 경우 5~6의 특징만으로 분류 정확도가 거의 100%에 가까운 결과를 보였다. 또한 본 논문에서는 실제 산업 현장에서 진동 신호 취득 시 포함될 수 있는 잡음을 고려하여 취득한 신호에 백색 잡음(white Gaussian noise)을 인위적으로 추가하여 실험한 결과 LSP, PARCOR, MFCC 순으로 잡음 환경에 강인한 특징 벡터임을 확인할 수 있었다. Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

3D real-time positioning for autonomous navigation using a nine-point landmark

The objective of this paper is to propose a new monocular-vision strategy for real-time positioning applications. This is an important aspect whose solution is still necessary in many autonomous landmark-based navigation systems that run in non-controlled environments. The method is based on the analysis of the properties of the projected image of a single pattern consisting of eight small dots belonging to the vertices of an octagon and one more dot in the centre of it. The paper discusses how the pose is calculated by using the parameters of the ellipse that best fits the dots of the pattern and the relative position of the dots in it. The first part of this document provides a qualitative comparison with regard to other similar approaches. The method presented here has several notable properties. Firstly, the pattern can be easily recognized in the image and, more importantly, works under occlusion and noise circumstances. Secondly, it is capable of working in real-time conditions and deals with ranges of 30–700 cm. Finally, the method can be used on different applications (mobile robots and augmented reality systems) and yields better accuracy than others. An extensive report on experiments conducted in real situations is shown at the end of the paper. In order to make our method more compelling, an experimental comparison under occlusion and noise conditions is also made with one of the most widely used pose solutions, the ARToolkit method. ► The method provides better results than some previous pose solutions. ► It is able to work under occlusion and noise circumstances with real-time response. ► It is suitable for augmented reality based navigation.

A Class of Spectrum-Sensing Schemes for Cognitive Radio Under Impulsive Noise Circumstances: Structure and Performance in Nonfading and Fading Environments

In this paper, we propose a class of spectrum-sensing schemes for cognitive radio with receive diversity. By employing the generalized likelihood ratio test (GLRT) in the detectors on the antenna branches and exploiting a nonlinear diversity-combining strategy, the proposed scheme exhibits better performance than conventional schemes in various fading and noise environments. Exact expressions of the detection and false-alarm probabilities of the proposed scheme are derived in nonfading and Nakagami fading channels with Gaussian noise. Through computer simulations, it is confirmed that the proposed scheme provides a significant performance gain over conventional schemes in impulsive noise environments.