Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Novel Sampling and Reconstruction Method for Non-Bandlimited Impulse Signals

To sample non-bandlimited impulse signals, an extremely high-sampling rate analog-todigital converters （ADC） is required. Such an ADC is very difficult to be implemented with present semiconductor technology. In this paper, a novel sampling and reconstruction method for impulse signals is proposed. The required sampling rate of the proposed method is close to the signal innovation rate, which is much lower than the Nyquist rate in conventional Shannon sampling theory. Analysis and simulation results show that the proposed method can achieve very good reconstruction performance in the presence of noise.

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals.However,it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions.This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning.To overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path.To address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components precisely.Via experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach.Meanwhile,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

An Introduction to Convex Optimization Theory in Communication Signals Recognition

In this paper,convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts,main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model,which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations,in contrast to D-S evidence inference method,this new method can also generate reasonable recognition results. Moreover,this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method,and it has much lower computation complexity than that of D-S evidence inference method. In addition,this new method has better recognition result,stronger anti-interference and robustness. Therefore,the convex optimization methods can be widely used in the recognition of communication signals.

Research on Estimation of Time Delay Difference in Passive Locating for Impulse Signal

Analyzed the relation between time delay difference and time delay estimation errors, based on the principles of three-point locating, a reformed threshold method for time delay difference estimation of impulse signals, called as amendment estimation for short, is developed by introducing channel equalization technique to its conventional version, named as direct estimation in this paper, to improve the estimation stability. After inherent relationship between time delay and phase shift of signals is analyzed, an integer period error compensation method utilized the diversities of both contribution share and contribution mode of concerned estimates is proposed under the condition of high precision phase lag estimation. Finally, a cooperative multi-threshold estimation method composed of amendment and direct estimations to process impulse signals with three thresholds is established. In sea trials data tests of passive locating, this method improves the estimation precision of time delay difference efficiently. The experiments verify the theoretical predictions.

Quantum-Classical Algorithm for an Instantaneous Spectral Analysis of Signals:A Complement to Fourier Theory

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum, not taken into account by Fourier theory and lets alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: 1) compact support with excellent energy output treatment, 2) low computational cost, O(N) for signals and O(N2) for images, 3) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

A Neural Network based Method for Detection of Weak Underwater Signals

Detection of weak underwater signals is an area of general interest in marine engineering.A weak signal detection scheme was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and an extended Kalman filter (EKF).In this method chaos theory was used to model background noise.Noise was predicted by phase space reconstruction techniques and RBF neural networks in a synergistic manner.In the absence of a signal, prediction error stayed low and became relatively large when the input contained a signal.EKF was used to improve the convergence rate of the RBF neural network.Application of the scheme to different experimental data sets showed that the algorithm can detect signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than -40d B.

DESIGN OF TERNARY CURRENT-MODE CMOS CIRCUITS BASED ON SWITCH-SIGNAL THEORY

By applying switch-signal theory, the interaction between MOS transmission switch-ing transistor and current signal in current-mode CMOS circuits is analyzed, and the theory oftransmission current-switches which is suitable to current-mode CMOS circuits is proposed. Thecircuits, such as ternary full-adder etc., designed by using this theory have simpler circuit struc-tures and correct logic functions. It is confirmed that this theory is efficient in guiding the logicdesign of current-mode CMOS circuits at switch level.

Extracting hidden weak sinusoidal signal with short duration from noisy data: Analytical theory and computational realization

Signal detection is both a fundamental topic of data science and a great challenge for practical engineering. One of the canonical tasks widely investigated is detecting a sinusoidal signal of known frequency ω with time duration T：I（t）=Acos ω t＋Γ（t）, embedded within a stationary noisy data. The most direct, and also believed to be the most efficient, method is to compute the Fourier spectral power at ω：B=｜2/T∫0^T I（t）e^iωtdt｜. Whether one can out-perform the linear Fourier approach by any other nonlinear processing has attracted great interests but so far without a consensus. Neither a rigorous analytic theory has been offered. We revisit the problem of weak signal, strong noise, and finite data length T=O（1）, and propose a signal detection method based on resonant filtering. While we show that the linear approach of resonant filters yield a same signal detection efficiency in the limit of T→∞, for finite time length T=O（1）, our method can improve the signal detection due to the highly nonlinear interactions between various characteristics of a resonant filter in finite time with respect to transient evolution. At the optimal match between the input I（t）, the control parameters, and the initial preparation of the filter state, its performance exceeds the above threshold B considerably. Our results are based on a rigorous analysis of Gaussian processes and the conclusions are supported by numerical computations.

LARGE SIGNAL THEORY OF OROTRON IN CONSIDERATION OF SPACE CHARGE FIELD

An approximate formula of space charge field of orotron in rectangular coordinate system,when one of the boundaries is infinite, is derived. Using this model the self-consistant large signal interactionof orotron with space charge is calculated, and the effect of this charge on the performance of the device isdiscussed.

FUNDAMENTAL COMMUNICATIONS THEORIES AND SIGNAL PROCESSING TECHNIQUES FOR AMORPHOUS CELLULAR SYSTEMS

The rapid developing of the fourth generation(4G)wireless communications has aroused tremendous demands for high speed data transmission due to the dissemination of various types of the intelligent user terminals as well as the wireless multi-media services.It is predicted that the network throughput will increase

MY FURTHERANCE OF SIGNAL PROCESSING THEORY &. ITS APPLICA-TION

The theory of digital signal treatment and its application is a discipline with wide and promising prospects, having made its debut in the 1960s. During the period from the end of the 1950s to the 1970s, I was engaged in teaching and research work in the fields of electronics,digital circuits and numerical control and participated in the development of China’s first numerical machine. At the end of the 1970s.I went to the United States

An ATMS data-driven method for signalized arterial coordination

In order to minimize the delays and stops caused by the early started coordinated green phase of the vehicle- actuated signal systems, a stochastic offsets calculation method based on the new types of advanced traffic management system （ATMS） data is proposed. As the mainline green starts randomly in vehicle-actuated signal systems, the random theory is applied to obtain the distribution of the unused green time at side streets based on the green gap-out mechanism. Then, the green start time of the mainline can be selected at the point with maximum probability to minimize the delays or stops caused by the randomly started mainline green. A case study in Maine, USA, whose traffic conditions are similar to those of the middle-size Chinese cities, proves that the proposed method can significantly reduce the travel time and delays.

Characteristics analysis of muzzle impulse noise based on wavelet energy spectrum

Because muzzle impulse noise could cause damage to or have an intluence on the operator, tiae ettecnve protecnve measures should be taken. Therefore, correct analysis of impulse noise characteristics is very significant. Considering the shortcomings of fast Fourier transform method （FFT） in analysis of muzzle impulse noise frequency characteristics, wavelet energy spectrum method is put forward. Based on specific experiment data, the frequency characteristics and spectral energy dis tribution can be obtained. The experiment results show that wavelet energy spectrum method is applicable in muzzle impulse noise characteristic analysis.

Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Multi-Source Data

In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Optimal Active Defence Using Dynamic Multi-Stage Signalling Game

Nowadays, security defence of network uses the game theory, which mostly applies complete information game model or even the static game model. To get closer to the actual network and defend actively, we propose a network attack-defence game model by using signalling game, which is modelled in the way of dynamic and incomplete information. We improve the traditional attack-defence strategies quantization method to meet the needs of the network signalling game model. Moreover, we give the calculation of the game equilibrium and analyse the optimal defence scheme. Finally, we analyse and verify effectiveness of the model and method through a simulation experiment.

Statistical Properties of a Ring Laser with Injected SIgnal and Backscattering

The statistical properties of a homogeneously broadened ring laser with an injected signal are investigated and the normalized two-mode intensity auto- and cross-correlation functions are calculated by a full saturation laser theory with backscattering. The theoretical predictions are in good agreement with the experimental measurements.Further investigation reveals that the backscattering can reduce the fluctuations in the system while the full saturation effect plays a major role when the laser is operated above threshold. It is also quite important to notice that the injected signal can drive the weak mode from incoherent light to coherent light.

Signal processing circuit of laser gyro based on FPGA and DSP

This is a paper about laser gyro sign a l processing circuit which is designed based on field-programmable gate array(FPGA) and digital signal processor(DSP).Through a pre-amplifier circuit,FPGA and DSP,a weak current signal is converted and transferred,then sent to the computer to display the final results.Through the laser gyro performance te sting,the obtained results coincide with those of the existing methods.Thus th e d esigned circuit realizes the function of laser gyro signal processing.

Analysis of Electronic Circuits with the Signal Flow Graph Method

In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.