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Machine learning has been widely used for solving challenging problems in diverse areas. However, to the best of our knowledge, seldom literature has discussed in-depth how machine learning approaches can be used effectively to “hunt” (identify) threats, especially advanced persistent threats (APTs) , in a monitored environment. In this study, we share our past experiences in building machine learning-based threat-hunting models. Several challenges must be considered when a security team attempts to build such models. These challenges include (1) weak signal, (2) imbalanced data sets, (3) lack of high-quality labels, and (4) no storyline. In this study, we propose Fuchikoma and APTEmu to demonstrate how we tackle the above-mentioned challenges. The former is a proof of concept system for demonstrating the ideas behind autonomous threat-hunting. It is a machine learning-based anomaly detection and threat hunting system which leverages natural language processing (NLP) and graph algorithms. The latter is an APT emulator, which emulates the behavior of a well-known APT called APT3, which is the target used in the first round of MITRE ATT&CK Evaluations. APTEmu generates attacks on Windows machines in a virtualized environment, and the captured system events can be further used to train and enhance Fuchikoma’s capabilities. We illustrate the steps and experiments we used to build the models, discuss each model’s effectiveness and limitations of each model, and propose countermeasures and solutions to improve the models. Our evaluation results show that machine learning algorithms can effectively assist threat hunting processes and significantly reduce security analysts’ efforts. Fuchikoma correctly identifies malicious commands and achieves high performance in terms of over 80% True Positive Rate and True Negative Rate and over 60% F3. We believe our proposed approaches provide valuable experiences in the area and shed light on automated threat-hunting research.

The on-line tool condition monitoring is demanded to detect the tool wear and to ensure the hole drilling process of printed circuit boards (PCB) goes on smoothly. However, due to the impact of ambient noise caused by the limited size of small drill and the laminated material of PCB, the tool wear signal features are too weak to extract. The stochastic resonance (SR) method has been proven to be effective in enhancing weak signals among various weak signal extraction. In this paper, an adaptive multistable stochastic resonance is presented to improve performance of the SR method and process the tool wear signals for PCB drilling. The differential evolution (DE) algorithm is applied to adaptively optimize potential parameters and compensation factor, which makes the SR method suitable for high frequency signal. Moreover, tool wear experiments with different drill wear are carried out to verify the effectiveness of the proposed method. The results indicate that the proposed method improves the signal-to-noise ratio and has great potential in enhancing weak signals for small drill condition monitoring in PCB drilling process.

This study presents the performance of the auto-correlation methods for detecting weak signals, where the signal level is much less than the noise level. Double and triple auto-correlation techniques are used to improve the detection performance compared with the single autocorrelation. Simulation results obtained by MATLAB programs show that the multiple correlation techniques outperform the single correlation in terms of probability of detection and probability of false alarm versus signal to noise ratio SNR.

Stochastic resonance can detect weak periodic signals from strong background noise without loss of signal energy. However, the classical bistable stochastic resonance has the inherent outp...

Stochastic resonance is the use of nonlinear systems to synchronize an original signal with noise. This method is commonly used to extract useful signals by reducing noise and has been widely used for mechanical weak fault diagnosis. This paper analyzes the characteristics of a periodic non-sinusoidal potential function, considers the shape of the model, and introduces a time-delay. The steady-state probability density function, effective potential function, and signal-to-noise ratio are then analyzed. As a result, a signal detection method for periodic non-sinusoidal time-delay stochastic resonance (PNTSR) is proposed. Experimental and engineering data are used to explain the PNTSR through the simulation. It is found that the PNTSR method has better fault detection results when compared to the classic bi-stable stochastic resonance method.

There are rich geothermal resources in China and they are widely distributed. After years of continuous exploration and production, most of the shallow geothermal resources have been explored and the current exploration mainly focuses on deep ones. Among a great many geophysical exploration methods, seismic survey is the most effective means of geothermal resource exploration and production, but its weak seismic reflection signal, low S/N ratio and poor imaging of thermal reservoirs due to the seismic geological conditions restrict the production and utilization of deep geothermal resources. It considers through the analysis of the geophysical characteristics of the thermal reservoir that the main causes for the weak seismic reflection signals from deep thermal reservoirs are (1) the shielding effect of the strong wave impedance interface between the thermal reservoir and the caprocks; (2) the small reflection coefficient inside the thermal reservoir; and (3) the serious absorption and attenuation of high-temperature fluids such as hydrothermal fluids and steam in the thermal reservoirs by seismic waves. Accordingly, a series of seismic data acquisition technologies are proposed based on the high-precision vibroseis low-frequency shooting, high-sensitivity geophones long-spread receiving and small bin size highfold technologies, and their application in the seismic acquisition of dry-hot rocks (HDR) in Gonghe Basin, Qinghai, shows a very good application effect.

Immunochromatographic assay (ICA) is widely applied in various fields. However, severe matrix interference and weak signal output present major challenges in achieving accurate and ultrasensitive detection in ICA. Here, a polydopamine (PDA)-mediated magnetic bimetallic nanozyme (Fe3O4@PDA@Pd/Pt) with peroxidase-like activity was synthesized and used as a probe in ICA. The magnetic property of Fe3O4@PDA@Pd/Pt enabled effective magnetic enrichment of targets, thereby reducing the matrix interference in the sample. PDA coating on the magnetic bimetallic nanozyme was employed as a mediator and a stabilizer. It improved the catalytic ability and stability of the magnetic bimetallic nanozyme by providing more coordination sites for Pd/Pt growth and functional groups (-NH and -OH). In addition, the Pd/Pt bimetallic synergistic effect could further enhance the catalytic ability of the nanozyme. A method was developed by integrating Fe3O4, PDA, and Pd/Pt into Fe3O4@PDA@Pd/Pt as a probe in ICA. With the proposed method, human chorionic gonadotropin and Escherichia coli O157:H7 were successfully detected to be as low as 0.0094 mIU/mL in human blood serum and 9 × 101 CFU/mL in the milk sample, respectively. This method may be readily adapted for accurate and ultrasensitive detection of other biomolecules in various fields.

Accurate traveltime of first arrivals is of great importance in investigating subsurface velocity information. A significant challenge preventing the picking of the first arrival, however, is that the recorded traces in complex mountain areas are often characterised by weak energy, strong noise and dramatic phase variation. The method of super-virtual refraction interferometry (SVI) is capable of retrieving and enhancing the weak first arrivals from those traces and attenuating the random noise. Unfortunately, the conventional SVI has equal-weighted stacking, and is susceptible to strong local noise. This paper introduces adaptive data-driven weights based on local similarity into SVI to solve this problem. Both near- and far-offset reference traces of high quality are technically selected for better preservation of useful information. Next, we develop some neighboring super-virtual traces in the stacking process for further enhancement of weak signals, which is a further extension and theoretically superior to conventional SVI in increasing the total stacking number. The successful applications of model and field data show the great advantages of our improved method. Compared with conventional SVI, our method has a better local noise suppression effect and stronger enhancement ability, especially at weak refractions. More importantly, it can provide a significant guarantee of higher quality data, thus distinctly achieving a more accurate and reliable traveltime in first arrival picking.

Space‐based automatic dependent surveillance‐broadcast (ADS‐B) is an important emerging augmentation of existing ground‐based ADS‐B systems. In this paper, the problem of space‐based ultra‐long‐range reception processing of ADS‐B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS‐B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high‐precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.

Particles moving in confined regions with bistable potential driven by weak signal and fluctuations may present a similar stochastic resonance (SR). This similar SR is called entropic stochastic resonance (ESR). The phenomenon of ESR in a confined asymmetrical bistable potential driven by a transverse periodic force is investigated when a non-Gaussian fluctuation is considered in the system. The left potential of the bistable system can be adjusted by introducing the asymmetrical ratio α. By the functional analysis and two-state theory, the approximate Signal-to-Noise ratio (SNR) is derived. It is found that there is a global maximum in the SNR curves when the asymmetrical ratio α and the noise intensity D are changed. It is also shown that the noise correlation time τ will suppress the ESR of the system and the departure q can obviously change the position of peak in the SNR. The above clues are helpful in achieving weak signal detection under irregular, small-scale systems.

Due to the complexity and unpredictability of the wireless communication environment, the existing carrier coarse estimation method will be greatly affected under low SNR conditions. Using the method of cyclic spectrum analysis, the carrier frequency of the BPSK signal at a lower signal-to-noise ratio can be estimated. But again, the cyclic spectrum consumes a lot of computing resources due to its large computational complexity. In this paper, the problem of downloading wave estimation with low SNR is solved by analyzing the cyclic spectrum. Through experiments, the suggestion of smooth window size selection of FAM algorithm is given under the condition of both computational efficiency and accuracy.

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.

Rolling bearing health status monitoring is essential for identifying early failures and avoiding un-planned downtime in industrial systems. To overcome the problems of existing entropy methods with multiple faults that are easily confounded at different scales, a weak signal feature extraction method based on refined composite multi-scale weighted entropy is proposed in this paper. The time–frequency domain features are constituted into a multi-dimensional original fault feature set, and the feature sensitivity is evaluated in terms of four feature evaluation criteria, in order to filter out a sensitive feature subset. Three types of refined composite multi-scale entropy are combined with sensitive feature parameters, in a weighted manner, through the use of the Hadamard product operation. The effects of different combinations of feature parameters on the refined composite multi-scale entropy are analyzed through experimental validation. According to the analysis of the experimental data from two test stations, the fault recognition rate reached 100% and 92.22%, respectively, based on the RCMWE method, starting from the first features. The results indicate that the proposed method can identify bearing fault types under different damage states at any scale, with the fault recognition rate being more stable than that of other methods. The proposed method can effectively distinguish rolling bearing health and fault states, providing higher classification accuracy for rolling bearing fault types and fault damage degrees. This puts forward a new idea for rolling bearing health state assessment, which has high engineering application value.

The kinetic behavier of the Duffing system is analyzed. The state changes of the Duffing system are studied. The main process is composed of the inner orbits state, the bifurcation state, the chaotic state, the chaotic critical state and the large-scale periodic state. Let the weak signal of the noise be the trigger condition that change the inner state of the Duffing system, the Duffing system applied in the weak signal detection is discussed. At last, the validity of this method is validated by the simulation.

In this paper, we propose a device that picks up a periodic but weak signal by amplifying it assisted by the existing background noise. The device consists of a doped layered semiconductor with three gates that generate a one-dimensional double-well potential along the semiconductor. A laser coolant is to be shined on the other side of the central gate perpendicular to the one-dimensional layer causing triple-well potential. A weak tunable oscillator imposed parallel to the layer that rocks the potential landscape can pick up an incoming signal of interest as a result of resonance. To justify the model, we carried out analytic calculation as well as Monte Carlo simulation. The two approaches agree reasonably well for all the different parameter values we used.

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends., 17 pages, 8 figures, 2 tables (preprint version)

Si-based blocked-impurity-band (BIB) terahertz array detector with response frequency 6THz has been realized. Measurement system and imaging system are built to characterize the comprehensive performance and imaging capabilities of the BIB detector. Noise equivalent power (NEP) of Si-based BIB array detector is measured to characterize its detection capability of weak signal. Imaging system based on Si-based BIB array detector is built and high-resolution imaging is realized.

Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.

Weak signal detection capability and real-time signal processing capability is significant for the underground space detection system. In this paper, an underground space detection system based on the channelization algorithm is designed. Through the channelization algorithm based on the weighted-overlap-add (WOLA) structure, the system can detect four weak signal energy values of different frequencies at one time. Simulation and experimental results show that compared with traditional detection methods, the system has a breakneck processing speed, and the weak signal detection ability can be improved.

Weak signal preservation is critical in the application of seismic data denoising, especially in deep seismic exploration. It is hard to separate those weak signals in seismic data from random noise because it is less compressible or sparsifiable, although they are usually important for seismic data analysis. Conventional sparse coding models exploit the local sparsity through learning a union of basis, but it does not take into account any prior information about the internal correlation of patches. Motivated by an observation that data patches within a group are expected to share the same sparsity pattern in the transform domain, so-called group sparsity, we have developed a novel transform learning with group sparsity (TLGS) method that jointly exploits local sparsity and internal patch self-similarity. Furthermore, for weak signal preservation, we extended the TLGS method and developed the transform learning with external reference. External clean or denoised patches are applied as the anchored references, which are grouped together with similar corrupted patches. They are jointly modeled under a sparse transform, which is adaptively learned. This is achieved by jointly learning a subset of the transform for each group data. Our method achieves better denoising performance than existing denoising methods, in terms of signal-to-noise ratio values and visual preservation of weak signal. Comparisons of experimental results on one synthetic data and three field data using the [Formula: see text]-[Formula: see text] deconvolution method and the data-driven tight frame method are also provided.

Distributed antenna arraying is a promising technology for weak signal reception. The received signals from different antennas are aligned and combined to improve the receiving signal-to-noise ratio (SNR). However, the combining performance is serious degraded by the difference of sampling frequency between antennas. In this paper, a frequency domain based signal combining method is proposed to solve this problem. The unaligned sampled data in time domain of the received signals are transformed to frequency domain using fast Fourier transform (FFT). The received signals can be aligned in frequency domain when their spectrum resolutions are the same. Therefore the received signals with the same total sampling time can be aligned and combined in frequency domain and then the combined signal is recovered using inverse fast Fourier transform (IFFT). Numerical simulations with two typical modulation types, i.e., PSK and PCM/BPSK/PM, prove the validity and robustness of this method.

This paper investigates the propagation of the weak aperiodic signal in a parallel array of FitzHugh–Nagumo (FHN) neurons with heterogenous aperiodic high-frequency (HAHF) disturbances. Different from the traditional vibrational resonance, where the optimal amplitudes of the HF driving signal should be tuned for individual elements, the ability of the proposed averaging parallel array network for weak signal detection can be optimized at a fixed amplitude of the HAHF disturbance, regardless of the nature of the input signal. Local connections in the parallel array network are found to be important for the propagation of weak signal in parallel array. Besides, the characteristics of high-frequency signal such as heterogeneity and frequency, can also modulate the propagation of aperiodic signal in parallel array.

To reduce the impact of attenuation and dispersion in ground penetrating radar (GPR) detection and to effectively improve GPR profile recording and to identify a weak detection target, a high-resolution processing method based on orthogonal matching pursuit and wavelet spectrum whitening (the OMWS method) is presented. First, according to the matching pursuit algorithm and the strong reflection-forming mechanism and based on sparse representation theory, a sparse dictionary suitable for the characteristics of a strong reflection signal was selected, and the signal was decomposed, which displayed the weak target signal well. Second, wavelet analysis was used to decompose the processed GPR signal into time-domain subsignals in different frequency bands, and each subsignal was whitened by a whitening filter. Then, the processed subsignals were reconstructed. The final results were compared with the results of conventional spectrum whitening method and showed that the OMWS method can accurately weaken the effect of the strong impedance interface and effectively enhance the local information of microcrack-reflected signals in both the time and frequency domains. The resolution of the processed GPR image is greatly improved, and the reflected signal of the hidden microcrack is easily visible. The method is clearer and more intuitive in the expression of the weak signals of hidden microcrack than the conventional spectrum whitening method, and the compensation for the high-frequency of the signals is more obvious.

The concept of weak signal (WS) was proposed by Igor Ansoff in the 1970s to describe early information about an emerging phenomenon in organizational environments. Surprising events can be avoided by methods for monitoring and analyzing these signals, continually. These methods enable the detection process of potential threats and opportunities that may arise from those surprising events. While this strategic process is very useful, conventional methods for WS monitoring and analysis do not avoid cognitive and motivational problems that can harm the competitive intelligence capacity of organizations. The solution of these problems can be related to methods that allow conducting the detection process collectively, quickly and with the least effort, through the eventual support of automatic tools. Ontologies, for example, are computational artifacts that represent organizational knowledge whose features can be used in a conceptual system for classification of WS. The purpose of this work is to present such system, which also uses Web crawling, competitive forces and PESTLE analysis in the detection of threats and opportunities. A case example also is discussed showing that this approach can be used as a viable alternative to support surveillance of strategic discontinuities that permeate the organizational environment.

The parameters of stochastic resonance (SR) system determine its output directly. Inappropriate parameters may lead to an unsatisfactory result even an error. To search the best parameter adaptively, a novel method is proposed in this paper. In the method, the relationships between each parameter are considered. Also, the Kramers rate of noise and some information about the weak signal are considered in our method. In the novel method, what we need to determine artificially is the interval of only one parameter. The others will be searched adaptively and optimally. Therefore, the proposed method is much different with traditional parameter-adaptive SR. The effectiveness and accurateness of our method are proofed by simulation, rotor crack detection and bearing inner diagnosis detection. The results compared with some other methods demonstrate our method can work more effectively. At last, we studied an interesting phenomenon founded in the process of our study and proposed a guide that the upper limitation of the Kramers rate should be equal to the target frequency after compressing when the re-scaling technology was used in SR system. Consequently, this paper makes a great contribution in the application of SR.

Stochastic resonance (SR), as a typical noise-assisted signal processing method, has been extensively studied in weak signal detection by virtue of the advantage of using noise to enhance the feature of periodic signal. And how to better apply the SR method in engineering signal processing has always been the research hotspot. In this paper, a novel adaptive SR method based on coupled bistable systems (ACBSR) is proposed to further improve the performance of SR method for weak signal detection. Compared with the cascaded bistable SR system, the suggested coupled bistable SR (CBSR) system can enhance the target signal while suppressing the low-frequency and high-frequency interferences, whose output behavior exhibits good bandpass filtering characteristic. In addition, a weighted spectral peak signal-to-noise ratio (WPSNR) index is constructed by using spectral peak SNR, spectral correlation coefficient and zero-crossing ratio to serve as the measurement index of SR for periodic signal detection. The constructed WPSNR index is simple in form and easy to calculate, which can quantitatively evaluate the resonance effect of periodic signal, even though the target signal frequency cannot be estimated accurately. And the combination of WPSNR index and the CBSR system is beneficial to improve the performance of SR method in rolling bearing fault diagnosis. Experiments and engineering application are performed to verify the effectiveness and superiority of the proposed ACBSR method in comparison with the traditional SR method.

In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions.

As one of the most important processes in steel wire rope inspection, defect signal processing is of great significance in guaranteeing safety and precision measurement. Aiming at the weak signal detection of steel wire rope with mixed strands and noise, a combined signal processing method based on magnetic flux leakage testing and multi-step filtering techniques are proposed in this paper. The experiments are first introduced and performed on three typical types of steel wire rope with diameters of 28 mm, 32 mm, 45 mm, and different wire broken defects detected under liftoff distances of 13 mm and 20 mm; the acquired signals are then analyzed both in time and frequency domain. According to the weak signal characterizations, the principle of the proposed methods and algorithm are given concretely. Afterwards, comparison of signal processing results between the traditional lowpass filtering, wavelet denoising, median filtering, and the proposed method are presented. Finally, the influence factors of smoothing types and moving average span of the proposed methods are investigated. The processing results of the proposed methods are shown through short-time Fourier transform and signal-to-noise ratio analysis, which not only demonstrates the validity and feasibility of the combined methods with the highest signal to noise ratio of 90.37 dB, but also exhibits a great potential of precision defect detection and practical application in steel wire rope inspection.

Apple is one of the most important fruit grown and consumed in the temperate climate region. About 2% of the European population suffers from several allergenic reactions after consumption. Presence of 7 members of Mal d 1 gene family occurring in some apples was examined by PCR. The Mal d 1.01 and 1.02 genes could be detected from 91% and 79% of apple cultivars, respectively, due to the high degree of conservative regions. The Mal d 1.04 gene has 4 functional varieties and 2 pseudo-alleles, so it is highly variable. The PCR amplifi cation with Mal d 1.06 primers gave one or two fragments with different sizes. The electrophoretic pattern is a suitable means to select apple cultivars according to their low, medium, or high Mal d 1 allergen content. Florina apple showed the single 154 bp allele, which is responsible for the small Mal d 1 allergen content in homozygote form.. Several samples gave weak signal or did not give any fragment-band on the gel, so Mal d 1.07 and 1.08 genes might have more varieties. Regarding the Mal d 1.09 gene we have found that it has conservative sequences in different apple cultivars and does not have too many varieties.

The emergence of the photoacoustic imaging (PAI) expands the application of biomolecules bioimaging in cells, various tissues, and living body to monitor multiple physiological processes in complex internal environments. The PAI possesses intriguing properties such as non-invasive, highly selective excitation, and weak signal attenuation. Especially, the near-infrared (NIR) PAI displays low optical absorption and scattering, good temporal or spatial resolution and deep penetration, holds great potential in biomedical applications. We briefly compare different imaging modalities to provide a comprehensive understanding of their characteristics and related applications, highlighting the feature of the PAI. The principle of PAI is then delineated and the emerging NIR-PAI is discussed. We then focus on elaboration of the recent achievement of typical NIR-PAI contrast and their biomedical applications, especially the strategies used to improve contrast rational design and PAI performance are summarized. The PAI-related multimodal imaging approaches for improving imaging accuracy are also covered in the review. Finally, the challenges and prospective are pointed out for attracting more researchers to accelerate the development of PAI.

The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matching. Unfortunately, when a graph matching algorithm fails to find the truth because of weak signal, there may be “phantom alignment strength” from meaningless matchings that, by random noise, have fewer disagreements than average (sometimes substantially fewer); this alignment strength may give the misleading appearance of significance. A practitioner needs to know what level of alignment strength may be phantom alignment strength and what level indicates that the graph matching algorithm obtained the true matching and is a meaningful measure of the graph correlation. The Phantom Alignment Strength Conjecture introduced here provides a principled and practical means to approach this issue. We provide empirical evidence for the conjecture, and explore its consequences.

GPS, as a widely used positioning tool, covers more aspects of people's daily life and people have been interested in the study of GPS signal acquisition algorithm for a long time. In this paper, a widely used signal acquisition algorithm with superior performance, PMF-FFT algorithm, is optimized. A fast GPS signal acquisition method based on fuzzy logic is proposed to solve the problem of signal acquisition failure caused by weak signal being ignored as noise under low SNR condition. Simulation results prove that the algorithm proposed in this paper can effectively capture signals and performs well under weak signals.

Sample barcoding is essential in mass cytometry analysis, since it can eliminate potential procedural variations, enhance throughput, and allow simultaneous sample processing and acquisition. Sample pooling after prior surface staining termed live-cell barcoding is more desirable than intracellular barcoding, where samples are pooled after fixation and permeabilization, since it does not depend on fixation-sensitive antigenic epitopes. In live-cell barcoding, the general approach uses two tags per sample out of a pool of antibodies paired with five palladium (Pd) isotopes in order to preserve appreciable signal-to-noise ratios and achieve higher yields after sample deconvolution. The number of samples that can be pooled in an experiment using live-cell barcoding is limited, due to weak signal intensities associated with Pd isotopes and the relatively low number of available tags. Here, we describe a novel barcoding technique utilizing 10 different tags, seven cadmium (Cd) tags and three Pd tags, with superior signal intensities that do not impinge on lanthanide detection, which enables enhanced pooling of samples with multiple experimental conditions and markedly enhances sample throughput.

There is a growing need to continue operating the Global Navigation Satellite Systems (GNSS) receivers under increasingly challenging and stressful conditions, where signal experiences deep fading, blockage, or high platform dynamics. As the most fragile component of the GNSS receiver, the carrier tracking loop must achieve improved tracking capability. The subject of GNSS tracking loop design has been well studied. This thesis takes the control system design perspective, presents tracking loop design as a state feedback/state estimator framework, sheds insight on frequency domain analysis, derives optimal parameters for carrier tracking loop design, and proposes adaptive tracking solutions for challenging environment. Two generalized carrier tracking loops, namely, the generalized phase tracking loop and the generalized frequency tracking loop, are studied using this state space framework. For the generalized phase tracking loop design, three approaches, i.e., proportional integral filter (PIF), Wiener filter (WF), and Kalman filter (KF), are presented in an unified manner from the state feedback/state estimator framework. With the state space framework, analytical equations characterizing the carrier phase tracking loop performance are derived. These equations relate the phase tracking error variance and dynamic stress phase error to the filter design parameters, such as integration time and noise equivalent bandwidth, as well as other parameters, such as thermal noise, oscillator noise, and receiver platform dynamics. From these equations, filter design parameters are optimized under various operating scenarios, such as weak signal or high dynamics. More specifically, the tracking sensitivities of the generalized phase tracking loop with these designs are obtained. Building on this analysis, an adaptive phase tracking scheme with time-varying integration time or loop bandwidth is proposed. A similar approach is applied to frequency tracking loop design. The PIF design is mapped to the state space structure through the equivalent closed-loop transfer function. Traditional KF-based frequency tracking loop design assumes white Gaussian noise. However, the frequency error measurement noise is non-white and so analytical equations for the frequency tracking error variance and dynamic stress frequency error are derived, taking into account the non-white noise characteristic. Frequency tracking error variance is used to evaluate the frequency tracking loop performance under the effects of thermal noise, oscillator noise, and platform dynamics. Using these analytical equations, optimal loop parameters are obtained, and the frequency tracking sensitivity is characterized. Based on these theoretical analysis, an adaptive frequency tracking scheme with loop bandwidth is proposed. Simulation results demonstrate the effectiveness of the proposed adaptive generalized carrier phase/frequency tracking architecture and verify the theoretical prediction. Doctor of Philosophy (EEE)

In this study, Stochastic resonance phenomenon in Morris-Lecar neurons was investigated. Obtained results showed that Stochastic resonance occurs in all classes of Morris-Lecar neuron. It is also observed that the weak signal detection performance of Type I neurons are significantly high compared the weak signal detection performance of Type II and III neurons at the optimal noise intensity. In addition, it is seen that the signal detection frequency range of Morris-Lecar neurons are similar for all classes. However, it was noticed that the threshold of amplitude ensuring firing in Type III neurons was higher than Type I and II neurons.

Rapid development of terahertz technologies causes constant search for new possibilities of making optical elements, needed for proper beam shaping. Large optical elements are often used in THz setups to collect as much power as possible. Unfortunately, traditional refractive optical lenses significantly absorb already weak signal. This is the reason for searching for new solutions in the form of diffractive optical elements (DOEs). For higher terahertz frequencies however, it is challenging to find an appropriate material as well as uncomplicated manufacturing methods. In this article, we present a higher-order kinoform (HOK) made of paraffin, cast from 3D-printed mold, efficiently working for 0.6 THz.

The normal diffusion effect is introduced as a new regulating factor into the established diffusive coupling model for bistable oscillator networks. We find that the response of the system to the weak signal is substantially enhanced by the anormal diffusion, which is termed anormal-diffusion-induced resonance. We also reveal that the diffusive coupling-induced transition, which changes the system from a bistable to a monostable state, is of fundamental importance for the occurrence of resonance. The proposed approach is validated using simulation studies and theoretical analyses. Our results suggest that diffusion induced resonance can be more easily observed in nonlinear oscillator networks.

A recently proposed deconvolution method applied to conventional beamforming (CBF) shows a much higher array gain (AG) than CBF in theory, thereby providing the possibility for detecting a weak signal with a much lower signal-to-noise ratio (SNR). However, simulated data processing shows an effective AG that decreases with decreasing SNR. The reason for the performance loss is analyzed. A method based on deconvolution of the signal subspace of the CBF outputs is shown to recover most of the AG loss. It is used to trace a weak signal in bearing and time.

The existing Duffing oscillator weak signal detection method will lead to the degradation of detection performance when the noise is strong. In order to reduce detection signal-to-noise ratio and improve the detection performance, a weak signal detection method based on Duffing oscillator phase transition rate is proposed. This method uses the phase transition rate of Duffing oscillator to judge, which can effectively reduce the detection error caused by pure noise, and further improve the performance of weak signal detection. Compared with the existing methods, the lower limit of detection signal-to-noise ratio is reduced by 2dB.

In this paper, we introduce the principle of identifying state transition based on the Van der Pol-Duffing oscillator in weak signal detection, which demonstrate the importance to indicate external perturbation existence and detect weak signal accurately. Starting from analyzing the insufficiency of employing classical identification methods occurring in the detecting process, we put forward a novel identification method named region analyzation method to improve the deficiency. Then, an establishment and feasibility analysis of this proposed method are described exhaustively. Numerical experiments on merits of this proposed method that is compared with the classical methods are carried out. The results indicate that this proposed method has better identifying capability than the classical methods and provides extendibility to engineering application.