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Synthetic sounds, tone-beeps, vowels or syllables are typically used in the assessment of attention to auditory stimuli because they evoke a set of well-known event-related potentials, whose characteristics can be statistically contrasted. Such approach rules out the use of stimuli with non-predictable response, such as human speech. In this study we present a procedure based on the robust binary phase-shift keying (BPSK) receiver that permits the real-time detection of selective attention to human speeches in dichotic listening tasks. The goal was achieved by tagging the speeches with two barely-audible tags whose joined EEG response constitutes a reliable BPSK constellation, which can be detected by means of a BPSK receiver. The results confirmed the expected generation of the BPSK constellation by the human auditory system. Also, the bit-error rate and the information transmission rate achieved in the detection of attention fairly followed the expected curves and equations of the standard BPSK receiver. Actually, it was possible to detect attention as well as the estimation a priori of its accuracy based on the signal-to-noise ratio of the BPSK signals. This procedure, which permits the detection of the attention to human speeches, can be of interest for new potential applications, such as brain–computer interfaces, clinical assessment of the attention in real time or for entertainment.

This paper investigates a new asymmetric bistable model driven by correlated multiplicative colored noise and additive white noise. The mean first-passage time (MFPT) and the signal-to-noise ratio (SNR) as the indexes of evaluating the model are researched. Based on the two-state theory and the adiabatic approximation theory, the expressions of MFPT and SNR have been obtained for the asymmetric bistable system driven by a periodic signal, correlated multiplicative colored noise and additive noise. Simulation results show that it is easier to generate stochastic resonance (SR) to adjust the intensity of correlation strength $\lambda$. Meanwhile, the decrease of asymmetric coefficient $r_2$ and the increase of noise intensity are beneficial to realize the transition between the two steady states in the system. At the same time, the twice SR phenomena can be observed by adjusting additive white noise and correlation strength. The influence of asymmetry of potential function on the MFPTs in two different directions is different.

It is difficult to extract weak signals in strong noise background, therefore a piecewise asymmetric exponential potential under-damped bi-stable stochastic resonance (PAEUBSR) system is proposed. First, the theoretical analysis of the steady-state probability density (SPD), mean first passage time (MFPT) and output signal-to-noise ratio (SNR) are derived under the adiabatic approximation theory. At the same time, the influence of different system parameters on system performance is explored. Then the PAEUBSR system is applied to the fault signal diagnosis of different types of bearings, and the parameters are optimized through the adaptive genetic algorithm (AGA). The test results are compared with the exponential potential over-damped symmetric bi-stable stochastic resonance (EOSBSR) system and the exponential potential under-damped symmetric bi-stable stochastic resonance (EUSBSR) system. Finally, the detection results on two sets of bearing fault data show that the PAEUBSR system has better effects on the enhancement and detection of bearing fault signals. This provides good theoretical support and application value for this system in subsequent theoretical analysis and practical engineering applications.

The watermark embedded by traditional methods is easy to be lost under some attacks. To overcome this problem, this study proposes a novel method based on DWT. It adopts a digital audio watermarking state-switching system which optimizes DWT coefficients doubly. Firstly, it combines the quantization-embedding system and the weights of DWT coefficients with SNR to obtain an optimization model for watermarking. Next, the Lagrange principle, Hessian matrix, and minimum energy play three essential roles to obtain the optimal DWT coefficients and weights. Moreover, the almost invariant feature of the optimal weights holds demonstrating resistance to amplitude scaling. Compared with similar algorithms, the experimental results verify that the embedded audio in the proposed method has higher signal-to-noise ratio (SNR) and lower bit error rate (BER). At the same time, it indicates stronger robustness against various attacks, such as re-sampling, amplitude scaling, and mp3 compression.

As the CMOS technology continues to scale to achieve higher performance, power dissipation and robustness to leakage and, process variations are becoming major obstacles for circuit design in the nanoscale technologies. Due to increased density of transistors in integrated circuits and higher frequencies of operation, power consumption, propagation delay, PDP, and area is reaching the lower limits. We have designed 16-bit adder circuit by Carry-Select Adder (CSA) using different pass-transistor logic. The adder cells are designed by DSCH3 CAD tools and layout are generated by Microwind 3 VLSI CAD tools. Using CSA technique, the power dissipation, PDP, area, transistor count, are calculated from the layout cell of proposed 16-bit adder for Ultra Deep Submicron feature size of 120, 90, 70, and 50 nm. The UDSM signal parameters are calculated such as signal to noise ratio (SNR), energy per instruction (EPI), Latency, and throughput using layout parameter analysis of BSIM 4. The simulated results show that the CPL is dominant in terms of power dissipation, propagation delay, PDP, and area among the other pass gate logics. Our CPL circuit dominates in terms of EPI, SNR, throughput, and latency in signal parameters analysis. The proposed CPL adder circuit is compared with reported results and found that our CPL circuit gives better performance.

In this paper, an approach to estimate signal to noise ratio (SNR) and effective number of bits (ENOB) in nonideal multi-bit stages of pipelined analog to digital converters (ADCs) is presented. The most significant error sources in multistage ADCs are the capacitor mismatch and the finite and imprecise gain of amplifier. Output voltage of each stage in pipelined ADC is modeled by an ideal and a nonideal output, where nonideal output is the error due to circuit imperfections in each stage. Using an appropriate model, the SNR and ENOB due to circuit nonidealities and in terms of standard deviation of random errors are calculated. Simulation results show the accuracy of the analytical proposed approach in estimation of SNR and ENOB in multi-bit per stage pipelined converters.

Dependences of the X-ray luminosity (L_x) of young single pulsars, due to ejection of relativistic particles, on electric field intensity, rate of rotational energy loss (Ė), magnetic field, period, and some other parameters of neutron stars are discussed. Influence of the magnetic field and effects of some other parameters of neutron stars on the L_x-Ė and the L_x-τ (characteristic time) dependences are considered. Evolutionary factors also play an important role in our considerations. Only the pulsars with L_{2–10 keV}>10³³erg/s have pulsar wind nebula around them. The pulsars from which γ-ray radiation has been observed have low X-ray luminosity in general.

We constructed a neural network of the hippocampus and proposed an adaptive learning rule of synapses to simulate the storing and retrieving processes of memory in the hippocampus by a mechanism of resonance. The hippocampus network consists of CA1, CA3 and DG, in particular, CA1 is a storage of memory, which receives inputs from both EC through perforant path (PP) and CA3 through Schaffer collaterals (SC). The stimulated results showed that the memory trace was unable to be encoded in CA1 when only a single subthreshold signal from EC or CA3 was inputted, of which the main reason might be lack of the resonance of the two signals. We calculated signal-to-noise ratio (SNR) of the network, and found it reached a peak value at appropriate SC connection strength, indicating that a typical stochastic resonance phenomenon appeared in PP signal detection. The inputs from EC and CA3 were able to enhance the memory representation in CA1, although still incomplete. We used a learning rule to modify synaptic weights by which the network could learn an external pattern. The hippocampus network tended to be stable after sufficient evolution. Some CA1 neurons show synchronized firings which are used to represent memory and are clearer than observed memory traces before learning. The model and results provide a good guidance to our understanding of the mechanism of the hippocampus memory.

The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from $-$20 to 20dB with 5dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

In this paper, a novel underdamped tri-stable stochastic resonance (NUTSR) is presented in order to overcome the low out-performance of weak signal reinforcement in the classical tri-stable stochastic resonance (CTSR). The two systems are compared using the input and output amplitude as a measure when noiseless. NUTSR improves the output saturation characteristics and has more outstanding signal enhancement capability. In the background of Gaussian noise, the expressions of mean first-pass time (MFPT), steady-state probability density (SPD) and signal-to-noise ratio (SNR) are derived. The fourth-order Runge–Kutta algorithm and genetic algorithm (GA) is used for numerical simulations. Then the numerical simulation results of the two systems are compared comprehensively, and the theoretical deduction and numerical simulation results of NUTSR are also compared. Moreover, the two systems are applied in two bearing fault types of 6205-2RS JEM SKF and HRB 6205-2Z. Finally, the feasibility of NUTSR is verified under different noise, which is simulated numerically under symmetric trichotomous that NUTSR has better noise immunity and the increase in signal amplitude is more pronounced.

A novel combined underdamped bistable stochastic resonance (CUBSR) is proposed in this paper. Under the noise-free condition, the output amplitude is used as a measurement index of classical bistable stochastic resonance (CBSR) and CUBSR, which demonstrate CUBSR does not have output saturation characteristics and has a more prominent signal enhancement capability. Then, the expressions of mean first-pass time (MFPT), steady-state probability density (SPD) and signal-to-noise ratio (SNR) are derived. Combined with the fourth-order Runge–Kutta algorithm and genetic algorithm (GA) for numerical simulations, the comparison of the theoretical derivation and numerical simulation of CUBSR can be verified. Then, the two systems are applied to the engineering application of bearing fault diagnosis. Finally, the multi-scale noise-modulated SR method based on wavelet packet transform is studied to overcome the limitation of traditional parameter modulation and to achieve SR detection at multiple frequencies. Simulation analysis and bearing fault diagnosis show that the method can effectively detect the multi-frequency weak signal submerged in noise, resulting in a significant enhancement in signal amplitude.

In stochastic resonance (SR) weak signal detection, there is no literature currently report on the study and comparison of one-dimensional (1D) and 2D pinning potential worldwide which has potential research necessity. A one (ODPPBSR) and a 2D pinning potential bistable SR (TDPPBSR) are proposed. The expressions for MFPT, SPD and SNR are derived based on adiabatic approximation theory. To investigate the correctness of the theoretical results, numerical simulations are carried out with the Runge–Kutta algorithm and the genetic algorithm (GA) is used to optimize the system. The system has exceptional ability to restore signal periodicity and amplitude amplification at low frequency, high frequency and multi-frequency. The two systems are compared of the detection capabilities on weak signals through wavelet transform denoising and applied to the 6205-2RS JEM SKF and HRB 6205-2Z for bearing fault detection. The experimental results show that the 2D system is superior to the 1D system.

Efficient $R$ peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective $R$ peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel $R$ peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true $R$ peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity $\mathrm{\text{(Se)}}=99.88\%$, Positive predictivity $\mathrm{\text{(+P)}}=99.78\%$ and Accuracy $\mathrm{\text{(Acc)}}=99.67\%$. On Fantasia Database involvement, $\mathrm{\text{Se}}=99.99\%$, $\mathrm{\text{+P}}=99.98\%$ and $\mathrm{\text{Acc}}=99.97\%$. On MIT-BIH Normal Sinus Rhythm Database involvement, $\mathrm{\text{Se}}=99.99\%$, $\mathrm{\text{+P}}=99.99\%$ and $\mathrm{\text{Acc}}=99.99\%$. Compared with other $R$ peaks detection methods, the proposed algorithm is simple, efficient and robust.

A novel photoelectric device-Lateral PIN photodiode gated by a transparent electrode (LPIN PD-GTE) fabricated on SOI film is proposed. Its physical model is presented based on standard semiconductor equations. In this device, recombination of carriers is ignored due to its operation in depletion region and high electric field strength (E > 1 × 10⁴V/m). Numerical calculation indicates that LPIN PD-GTE has high sensitivity and SNR (Signal to Noise Ratio). This model allows one to predict and optimize the photoelectric characteristics of LPIN PD-GTE.

Low-light camera is an indispensable component in various fluorescence microscopy techniques. However, choosing an appropriate low-light camera for a specific technique (for example, single molecule imaging) is always time-consuming and sometimes confusing, especially after the commercialization of a new type of camera called sCMOS camera, which is now receiving heavy demands and high praise from both academic and industrial users. In this tutorial, we try to provide a guide on how to fully access the performance of low-light cameras using a well-developed method called photon transfer curve (PTC). We first present a brief explanation on the key parameters for characterizing low-light cameras, then explain the experimental procedures on how to measure PTC. We also show the application of the PTC method in experimentally quantifying the performance of two representative low-light cameras. Finally, we extend the PTC method to provide offset map, read noise map, and gain map of individual pixels inside a camera.

NMR is one of the important analytic tools which is used to obtain certain information such as metabolic concentrations in neural or muscular tissues. In some other important applications such as proton decoupling, it is necessary to design NMR transmitters/receivers capable of operating at multiple frequencies, while maintaining a good performance at each frequency. In this work, a new nuclear magnetic resonance (NMR) receiver microcoil based on MEMS technology is proposed. The designed structure uses MEMS microswitches with low contact resistance and NMR-based actuation mechanism. The proposed device can detect carbon (13C), proton (1H), and phosphorus (31p) nucleus with larmor frequencies of 96.36MHz, 383MHz, and 155.11MHz at 9 T magnetic field, respectively. The designed microcoil achieves three important goals:

• (1)Getting high SNR, high Q and high filling factor which are key parameters in NMR performance, by changing number of turns.
• (2)Turning into the array of microcoils to obtain better SNR.
• (3)Turning into two or three microcoils inside of each other for simultaneous detection.

The MEMS microswitch in this paper uses static magnetic field of the NMR for its operation ($B_0=9$T) which simplifies the switch mechanism. This switch is small (150$\mu$m$\times$50$\mu$m$\times$6$\mu$m), scattering parameters of 43.2db isolation and 0.0059 insertion loss and maximum displacement of 2.03$\mu$m due to the magnetostatic actuation. In this work, the models and investigations are conducted using finite element simulations in COMSOL Multiphysics. The switch scattering parameters are obtained by HFSS 12.0.

Image de-noising is an essential tool for removing unwanted signals from an image. In Computed Tomography (CT) images, the image quality is degraded by the absorption of X-rays and quantum noise, which is generated due to the excitement of X-ray photons. Removal of noise and preservation of information in the CT images becomes a challenge for an imaging algorithm design. During the algorithm design selection of dataset is an important aspect for deducing results. The dataset used in this research comprises of 60 CT scan images of liver cancer archived from the arterial contrast enhanced phase. In this phase the cancer cells appear more intense as compared to the healthy liver tissue due to the absorption of contrast enhancing reagent. The experimentation for appropriate noise removal filter selection is done by testing the images using Mean, Median and Weiner Filters. The filter selected should give an image output which has minimal randomness, sharper boundaries and no blur. The de-noised image will provide a better visibility of the disease to the radiologist and physician. The performance parameters used for the assessment of various filters used in the study include visual assessment, entropy and signal to noise ratio (SNR) of the images. Median filter gives an accuracy of 96%, mean filter is 76.2% accurate with respect to original information and Weiner filters has an accuracy of 79.7%.

A new algorithm for speech enhancement based on wavelet shrinkage method is presented in this paper. First, the noisy speech by the Bark-scaled Wavelet Packet (BS-WPD) is decomposed to simulate the human auditory characteristics. Then a new thresholding algorithm which has many advantages over soft and hard thresholdings put forward by D.L. Donoho and I.M. Johnstone is proposed. Simulation results indicate that this new method is very useful and efficient in the process of white noise reduction from speech, and the new thresholding algorithm gives better SNR improvement than other traditional thresholding algorithms.

Commom method have low sorting rates and is rensitive to the Signal Noise Radio (SNR), thus, resemblance coefficient of bispectrum two dimensions characteristic is applied to sort unknown complicated radar signal to obtain high sorting rate. The bispectrum of received signal is extracted, and it is predigested to two dimensions characteristic. A rectangle pulse sequence and triangle pulse sequence are constructed. Then, the resemblance coefficient of two dimensions characteristic with rectangle pulse sequence and triangle pulse sequence are gained, and they are used as the sorting parameters. The bispectrum of different signal is distinguishing and it is not sensitive to SNR and the resemblance coefficient is divisible and steady. The advantage of this new method is validated by simulation results and the sorting rate is not less than 86% at SNR of 5 dB.