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Objective: Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features. Methods: Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and "UNSW" R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database. Results: For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% on the synthesized datasets with the signal-to-noise ratio (SNR) of 6 dB. With the same datasets, the Se of FB algorithm increased about 9.29% and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% on the synthesized one-heartbeat datasets. Conclusion: Our method effectively improves the performance of cardiac application algorithms in noisy environments. It is the first study to utilize a DNN to capture noisy segments of the ECG signal. Significance: Too many false alarms can cause alarm fatigue. Our method can be utilized as the pre-processing before signal analysis and thereby reducing false alarms from the ECG diagnostic systems.

Objective: Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach: PCG segments of 5 s duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 s segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation. Normalised spectral amplitude was determined by fast Fourier transform and wavelet entropy by wavelet analysis. For each of these a simple single feature threshold-based classifier was implemented and the frequency/scale and thresholds for optimum classification accuracy determined. The analysis was then repeated using relatively noise free 5 s segments (seg 2) of each recording. Spectral amplitude and wavelet entropy features were then combined in a classification tree. Main results: There were significant differences between normal and abnormal recordings for both wavelet entropy and spectral amplitude across scales and frequency. In the wavelet domain the differences between groups were greatest at highest frequencies (wavelet scale 1, pseudo frequency 1 kHz) whereas in the frequency domain the differences were greatest at low frequencies (12 Hz). Abnormal recordings had significantly reduced high frequency wavelet entropy: (Median (interquartile range)) 6.63 (2.42) versus 8.36 (1.91), p < 0.0001, suggesting the presence of discrete high frequency components in these recordings. Abnormal recordings exhibited significantly greater low frequency (12 Hz) spectral amplitude: 0.24 (0.22) versus 0.09 (0.15), p < 0.0001. Classification accuracy (mean of specificity and sensitivity) was greatest for wavelet entropy: 76% (specificity 54%, sensitivity 98%) versus 70% (specificity 65%, sensitivity 75%) and was further improved by selecting the lowest noise segment (seg 2): 80% (specificity 65%, sensitivity 94%) versus 71% (specificity 63%, sensitivity 79%). Classification tree with combined features gave accuracy 79% (specificity 80%, sensitivity 77%). Significance: The feasibility of accurate classification without segmentation of the characteristic heart sounds has been demonstrated. Classification accuracy is comparable to other algorithms but achieved without the complexity of segmentation.

Methods for source detection in high noise environments are important for single-photon emission computed tomography medical imaging and especially crucial for homeland security applications, which is our main interest. In the latter case, one deals with passively detecting the presence of low emission nuclear sources with significant background noise (with signal to noise ratio 1% or less). In passive emission problems, direction sensitive detectors are needed, to match the dimensionalities of the image and the data. Collimation, used for that purpose in standard Anger γ-cameras, is not an option. Instead, Compton γ-cameras (and their analogs for other types of radiation) can be utilized. Backprojection methods suggested before by two of the authors and their collaborators enable detection in the presence of a random uniform background. In most practical applications, however, cargo packing in shipping containers and trucks creates regions of strong absorption and scattering, while leaving some streaming gaps open. In such cases backprojection methods prove ineffective and lose their detection ability. Nonetheless, visual perception of the backprojection pictures suggested that some indications of presence of a source might still be in the data. To learn such features (if they do exist), a deep neural network approach is implemented in 2D, which indeed exhibits higher sensitivity and specificity than the backprojection techniques in a low scattering case and works well when presence of complex cargo makes backprojection fail completely.

A photoplethysmographic (PPG) signal can provide very useful information about a subject's cardiovascular status. Motion artifacts (MAs), which usually deteriorate the waveform of a PPG signal, severely obstruct its applications in the clinical diagnosis and healthcare area. To reduce the MAs from a PPG signal, in the present study we present a comb filter based signal processing method. Firstly, wavelet de-noising was implemented to preliminarily suppress a part of the MAs. Then, the PPG signal in the time domain was transformed into the frequency domain by a fast Fourier transform (FFT). Thirdly, the PPG signal period was estimated from the frequency domain by tracking the fundamental frequency peak of the PPG signal. Lastly, the MAs were removed by the comb filter which was designed based on the obtained PPG signal period. Experiments with synthetic and real-world datasets were implemented to validate the performance of the method. Results show that the proposed method can effectively restore the PPG signals from the MA corrupted signals. Also, the accuracy of blood oxygen saturation (SpO2), calculated from red and infrared PPG signals, was significantly improved after the MA reduction by the proposed method. Our study demonstrates that the comb filter can effectively reduce the MAs from a PPG signal provided that the PPG signal period is obtained.

Surface electromyography (EMG) signal from trunk muscles is often contaminated by electrocardiography (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of 'clean' experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from -10 to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p < 0.001 for any of the examined SNRs except 10 dB).

Objective Noninvasive fetal electrocardiography is emerging as a low-cost and high-accuracy technology for fetal cardiac monitoring. Signal processing techniques have been used over the past fifty years in this domain. The current major challenges of this domain, addressed in this study are (1) fetal electrocardiogram (fECG) extraction from few numbers of maternal abdominal channels in low signal-to-noise ratios; (2) online fECG extraction; (3) automatic and online signal quality assessment and channel selection; and (4) accurate and robust fetal R-peak detection and ECG parameter extraction. Approach Based on the theory of cyclostationarity, auxiliary maternal ECG channel(s) are synthetically constructed and augmented with the input channels. The augmented data are used to develop a robust multichannel source separation algorithm for online/offline fECG extraction, from as few as a single channel, and an accurate fetal R-peak detector using a two-pass matched filter. Several robust signal quality indexes (SQI) and a voting strategy are also proposed for automatic fetal signal quality assessment. Main results It is shown that the fECG and the fetal R-peaks can be accurately extracted from standard online available datasets, for which classical source separation methods (requiring many channels) had previously failed. The signal quality indexes fully automate the extraction and channel selection procedure. Finally, the proposed R-peak detector is highly robust to background noise and residual maternal R-peak components. Significance The proposed methods for fECG extraction, R-peak detection and automatic channel selection are evaluated (visually and numerically), on two online available datasets and compared with recently developed algorithms. The proposed algorithm is statistically shown to outperform the benchmarks in terms of average and standard deviation.

Sensitivity is an important performance characteristic of positron emission tomography (PET) systems. Improved sensitivity can be used to reduce injected dose, reduce scan time, or improve the signal-to-noise ratio and temporal resolution for dynamic studies. One way to improve the sensitivity of PET scanners is to reduce the gaps between detector modules. In this paper, a new signal processing method, named the shared-photodetector readout method, is proposed and evaluated. In this method, the signals generated in nearest neighbor photodetectors adjacent to the detector module of interest, were used to help identify the interaction location in the detector module of interest. Using this method, scintillator array-based detector modules with almost 100% packing fraction can be built, and the edge crystals can be clearly resolved, even when the crystals are small compared to the photodetector size. To evaluate this signal processing concept in one dimension, a detector block with four dual-ended readout detector modules, was designed. The detector block consisted of eight 4 × 4 arrays of SensL MicroFJ-30035 SiPMs coupled to both ends of a 14 × 56 array of 0.9 × 0.9 × 20 mm3 LYSO elements with a pitch size of 0.96 mm and a length of 20 mm. Performance in terms of energy resolution, flood histogram, timing resolution and depth-of-interaction resolution obtained using the shared-photodetector readout method were compared to those obtained using a conventional readout method. The results show that better over-all performance was achieved using the shared-photodetector readout method, especially at the edges and corners of the array.

ObjectiveA listener’s neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment. Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listener’s attention. A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy. Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively. The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy.Approach28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from 2 different locations, along with surrounding multi-talker background babble. EEG signals of the subjects were recorded while they focused on one story and ignored the other. The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations. Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30s segment of data. Behavioral speech recognition thresholds were obtained for the different speaker separations.Main resultsBoth the background noise level and the angular separation between speakers affected attention decoding accuracy. Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level. We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking. Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased. A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions.SignificanceThis work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy. It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions. It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses.Index TermsAuditory attention detection, EEG processing, neuro-steered auditory prostheses, brain-computer interface, cocktail party, acoustic conditions.The work is funded by KU Leuven Special Research Fund C14/16/057 and OT/14/119, FWO project nrs. 1.5.123.16N and G0A4918N, the ERC (637424) under the European Union’s Horizon 2020 research and innovation programme, and a research gift of Starkey Hearing Technologies. The scientific responsibility is assumed by its authors.

Cuff electrodes are effective for chronic electroneurogram (ENG) recording while minimizing nerve damage. However, the ENG signals are usually contaminated by electromyogram (EMG) activity from the surrounding muscles, stimulus artifacts produced by the electrical stimulation and noise generated in the first stage of the neural signal amplifier. This paper proposed a new cuff electrode to reduce the interference from EMG signals and stimulus artifacts. As a result, when an additional middle electrode was placed at the center of the cuff electrode, a significant improvement in the signal-to-interference ratio was achieved at 11% for the EMG signals and 12% for the stimulus artifacts when compared to a conventional tripolar cuff. Furthermore, a new low-noise amplifier was proposed to improve the signal-to-noise ratio. The circuit was designed based on a noise analysis to minimize the noise, and the results show that the total noise of the amplifier was below 1 μV for a cuff impedance of 1 kΩ and a frequency bandwidth of 300 to 5000 Hz.

The aim of this study is to analyse an effective wavelet method for denoising and tracking temporal variations of the auditory brainstem response (ABR). The rapid and accurate extraction of ABRs in clinical practice has numerous benefits, including reductions in clinical test times and potential long-term patient monitoring applications. One method of achieving rapid extraction is through the application of wavelet filtering which, according to earlier research, has shown potential in denoising signals with low signal-to-noise ratios. The research documented in this paper evaluates the application of three such wavelet approaches on a common set of ABR data collected from eight participants. We introduced the use of the latency-intensity curve of ABR wave V for performance evaluation of tracking temporal variations. The application of these methods to the ABR required establishing threshold functions and time windows as an integral part of the research. Results revealed that the cyclic-shift-tree-denoising performed superior compared to other tested approaches. This required an ensemble of only 32 epochs to extract a fully featured ABR compared to the 1024 epochs with conventional ABR extraction based on linear moving time averaging.

© 2018 Institute of Physics and Engineering in Medicine. Objectives: We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. Approach: Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. Main results: On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. Significance: The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

Three fundamental problems about the vortex flow sensor—bluff body design, signal detection method and signal processing method—have been mainly discussed by a number of researchers in recent years. However, another significant thing is that the position for vortex signal detection, the location of a piezoelectric pressure probe in a vortex flow sensor, is seldom investigated, which is the focus of this paper. It is known that the quality of vortex signals is directly related to the probe's location. Therefore, the low limit of measurement for a vortex flow sensor can be expanded by searching the optimum position for detection. Experiments were carried out in a water channel to analyze the pressure signals downstream of the bluff body. The vortex signal intensity, frequency, signal-to-noise ratio and uncertainty of the Strouhal number at each position were discussed. Finally, the optimum regions for vortex signal detection (probe location) for three bluff bodies were given: the distance between the bluff body and the position for detection is 50–75 mm when the width of the bluff body d = 14 mm, the distance is 75–125 mm when d = 22.5 mm and the distance is 75–150 mm when d = 28 mm. Meanwhile, numerical simulations on flow field were adopted to explain the experimental results.

Plasma facing components (PFC) in Tore Supra and W7X adopt the flat tile concept using carbon fibre composite (CFC) material for the plasma facing material. As the cooling structure is made of a copper alloy material (CuCrZr), the bonding technique between CFC tiles and CuCrZr is critical. Currently, a soft metallic compliant layer is interposed between the two; in such a way the significant thermal expansion mismatch between carbon and copper can be accomodated. The development of a reliable non-destructive inspection technique (NDT) for the bond, to be performed during the manufacturing process, is obviously of great importance. The SATIR (infrared thermography) test bed operating at Commisariat a l'Energie Atomique (CEA) Cadarache performs this function using transient infrared thermography: the thermal excitation is realized in the cooling channel and the presence of a faulty tile is detected in the form of a delayed thermal response. With this technique, the evolution of the surface temperature of an inspected element was compared to that of a defined free-defect element, using the so-called DTref criterion (maximum of the transient temperature difference). The defect detection capability of the SATIR test bed can be improved using signal processing methods. A first treatment based on spatial image autocorrelation allows a better localization of the bond defect. Moreover, the problem of detection and classification of random signals (like the thin defect signature) can be solved maximizing the signal-to-noise ratio (SNR). Two filters maximizing this ratio were optimized: the stochastic matched filter (SMF) aims at defect detection, while the constrained SMF aims at defect classification. These methods assume that the second-order properties of the process at play are known, through covariance matrices. All these methods process the SATIR signal utilizing any free-defect element as reference signal. The tile temperature signal is either processed by itself or compared to a numerical temperature evolution evaluated by finite element calculations. In view of ITER PFCs qualification, the possibility to extend all these methods from the flat tile concept to the monobloc target element concept is proved.

Lamb waves have great potential for structural damage detection in many engineering areas. However, the method requires some experience when complex structures are monitored. Deciding the optimal positions of transducers is one of the most important problems. Sensor location studies can be investigated using a number of signal processing techniques. This paper applies wave propagation modelling for the analysis of sensor positions in Lamb wave based damage detection. The problem is investigated using the local interaction simulation approach for Lamb wave propagation modelling in an aluminium plate with a rectangular damage slot and a fatigue crack. The study demonstrates that sensor location is important for clarity and physical understanding of propagating wave packages, good signal-to-noise ratio and reliable damage detection. All these elements can be investigated using numerical simulations.

The success of the guided-wave damage inspection technology depends not only on the generation and measurement of desired waveforms but also on the signal processing of the measured waves, but less attention has been paid to the latter. This research aims to develop an efficient signal processing technique especially suitable for the current guided-wave technology. To achieve this objective, the use of a two-stage matching pursuit approach based on the Gabor dictionary is proposed. Instead of truncated sine pulses commonly used in waveguide inspection, Gabor pulses, the modulated Gaussian pulses, are chosen as the elastic energy carrier to facilitate the matching pursuit algorithm. To extract meaningful waves out of noisy signals, a two-stage matching pursuit strategy is developed, which consists of the following: rough approximations with a set of predetermined parameters characterizing the Gabor pulse, and fine adjustments of the parameters by optimization. The parameters estimated from measured longitudinal elastic waves can be then directly used to assess not only the location but also the size of a crack in a rod. For the estimation of the crack size, in particular, Love's theory is incorporated in the matching pursuit analysis. Several experiments were conducted to verify the validity of the proposed approach in damage assessment.

A piezoelectric based built-in diagnostic technique has been developed for monitoring fatigue crack growth in metallic structures. The technique uses diagnostic signals, generated from nearby piezoelectric actuators built into the structures, to detect crack growth. It consists of three major components: diagnostic signal generation, signal processing and damage interpretation. In diagnostic signal generation, appropriate ultrasonic guided Lamb waves were selected for actuators to maximize receiving sensor measurements. In signal processing, methods were developed to select an individual mode for damage detection and maximize signal to noise ratio in recorded sensor signals. Finally, in damage interpretation, a physics based damage index was developed relating sensor measurements to crack growth size. Fatigue tests were performed on laboratory coupons with a notch to verify the proposed technique. The damage index measured from built-in piezoceramics on the coupons showed a good correlation with the actual fatigue crack growth obtained from visual inspection. Furthermore, parametric studies were also performed to characterize the sensitivity of sensor/actuator location for the proposed technique.

In the processing of temperature and atmospheric pressure data observed in an underground laboratory, the cross-correlation equilibrium method is proposed for determining the phase of a signal with a particular frequency. This method effectively suppresses the influence of white noise and reduces the phase errors derived from higher order harmonics and any direct current component in the experimental signal. The upper limit of phase error due to the other frequencies in the experimental data is estimated. Theoretical analysis and the result of computational simulation show that the signal-to-noise ratio of the phase determined is improved significantly.

This paper describes an advanced size and spatial distribution measurement technique for small spherical droplets and bubbles in two-phase flows. In the field of spray analysis, various kinds of measurement techniques have been proposed. Interferometric laser imaging for droplet sizing (ILIDS) is the method that can provide an instantaneous spatial distribution of droplet size. The number of fringes on the projected images by the ILIDS technique is correlated to their individual particle sizes. The measurement accuracy depends upon the image processing technique for estimating the size of the circular image and the fringe spacing. The relation between the fringe number and the particle diameter was derived by noting the angular phase difference of the external reflection and the direct refraction. Conventional ILIDS techniques, which observed the circular image with fringes on a film, have difficulties in discriminating the fringe images due to the overlap between them. Moreover, enlargement of the defocused image for measuring the larger particle caused the complicated overlapping. The objective of the present study is to describe the advanced ILIDS image acquisition optics and the accelerated data processing by employing a high resolution CCD camera and automated computing system. In the technique presented, the circular fringes are optically compressed into linear images that are horizontally defocused and vertically focused. The present partial compression technique, which is the integration of the vertical information of the image, reduced the noise involved and consequently the signal to noise ratio of the interference signals become much better than that of the conventional technique. The linear interferograms were easily detected by scanning the horizontal pixels of the recorded image. The signal processing procedure to determine the particle diameter needs only one-dimensional information for calculating the fringe spacing by discrete Fourier transforms. The present ILIDS technique was examined for the planar measurement of size and location of spray droplets, and the size transition of CO2 bubbles by absorption in water.

A method measuring the depth of liquid surface of oil well based on the resonance principle of air column is proposed in this paper. In the method, air resonance in casing pipe is inspired by sending white noise to oil well continuously, and then dynamic liquid level of oil well can be calculated by analysing frequency spectrum of the experimental resonance signal. Aiming at the problem that the resonance signal contains a lot of white noise, firstly, convolution window function with stronger filtering characteristics is used to improve the signal to noise ratio; then the method of Welch multi segment average power spectrum is used to eliminate noise interference further in frequency domain; finally, the real resonance frequency is measured on the base of the smooth and clear Welch power spectrum, and the depth of the oil well can be estimated effectively. The experimental results show that the above signal processing method can greatly improve the accuracy of measuring the dynamic liquid level depth.

Proton precession magnetometer is a high-precision device for weak magnetostatic field measurement. The measurement accuracy depends on the frequency measurement of free induction decay (FID) signal, while the signal to noise ratio (SNR) is an important factor affecting the results. Many signal processing methods have been proposed to improve the SNR of FID signal. However, the theoretical analysis of different types of noises for FID signal has not be conducted yet. In addition, the relationship between the frequency measurement accuracy and SNR has not been explicitly established and quantified. This paper first proposes a background noise model based on the extracted features from the FID signal. With this model, background noises, such as white noise, narrow-band noise, and phase noise etc., can be calculated and estimated. Secondly, the relationship between the frequency measurement accuracy and SNR is identified. We also built a prototype proton magnetometer for field tests and validation purpose. Experiments were conducted to investigate this relation through simulation. Different values for frequency accuracy were obtained with different SNRs from the acquired FID signals from field tests. The consistence between the measurement and computational results is observed. When SNR is larger than 30 dB, the absolute frequency accuracy becomes constant which is about 0.04 Hz. With the stability taken into account, the accuracy can be better even when the SNR is higher than 30 dB. This study provides a reference to optimize the design of proton precession magnetometer and the frequency calculation for FID signal.

In active structural health monitoring (SHM) methods, it is necessary to excite the structure with a preselected signal. More studies in the field of active SHM are focused on applying SHM on higher frequency ranges since it is possible to detect smaller damages, using higher excitation frequency. Also, to increase spatial domain of measurements and enhance signal to noise ratio (SNR), the amplitude of excitation signal is usually amplified. These issues become substantial where piezoelectric transducers with relatively high capacitance are used and consequently, need to utilize high power amplifiers becomes predominant. In this paper, a novel method named Step Excitation Method (SEM) is proposed and implemented for Lamb wave and transfer impedance-based SHM for damage detection in structures. Three different types of structure are studied: beam, plate and pipe. The related hardware is designed and fabricated which eliminates high power analog amplifiers and decreases complexity of driver significantly. Spectral Finite Element Method (SFEM) is applied to examine performance of proposed SEM. In proposed method, by determination of impulse response of the system, any input could be applied to the system by both finite element simulations and experiments without need for multiple measurements. The experimental results using SEM are compared with those obtained by conventional direct excitation method for healthy and damaged structures. The results show an improvement of amplitude resolution in damage detection comparing to conventional method which is due to achieving an SNR improvement up to 50%.

The quality of a single-domain nanoparticle as a signal processing device is estimated with the aid of the signal-to-noise ratio (SNR) characteristic. In the absence of an external field, the magnetic moment of a uniaxial particle flips spontaneously between two anti-parallel equilibrium positions ensuing customary superparamagnetism. On imposition of an external ac field, it, together with random noise, influences the magnetic switching and the SNR can be found in the framework of micromagnetic theory. The main material parameters essentially involved in the resulting expression are, the N´ eel relaxation time, τN , the precessional decay time, τ0, and also the magnetic viscosity coefficient, ηm. In the paper we find these quantities by measuring the complex magnetic susceptibility as a function of frequency and polarizing field. Using these data, we estimate the SNR for the nanometre-size grains of a cobalt ferrofluid.

Hybrid reconstruction techniques have been introduced for the volume reconstruction of axially truncated cone-beam computed tomography projection data acquired along a circular source-detector trajectory. The introduction of weighted half-scan techniques into this framework is described in this paper. Due to the cone-beam geometry it is not possible to perform the weighting on the projections as is typically done in conventional single-line computed tomography. Hence, in this paper we present an efficient way to incorporate angular weighting functions, depending on the object point position, into the framework of hybrid cone-beam reconstruction. Four different angular weighting functions are introduced and discussed with respect to their cone-beam artefact behaviour and their influence on the signal-to-noise ratio. As a result, the most effective angular weighting function for hybrid circular cone-beam reconstruction is determined by means of a simulation study based on mathematical phantoms and clinical data sets. This distance-weighted angular weighting scheme yields the best results in terms of high image quality, low computational complexity and signal-to-noise variations in the reconstruction volume.

The standard deviation of the centre frequency of a signal is investigated for the quadrature demodulation technique (QDT). Signal frequencies can be measured by QDT unaffected by the amount of available signal periods. It can be used for the measurement of nonstationary signals generated by laser Doppler velocimeters. The dependence of the frequency measuring error on the averaging time of noisy single-tone Gaussian pulse signals is analysed. Assuming a quantum noise process, it is shown that the minimum measuring error results for an averaging time of approximately 1/ e 2 of the pulse duration of the signal. Alternatively, defined weighting of the measured values leads to a monotonically decreasing measuring error with increasing averaging time until the Cramer–Rao lower bound is reached. Therefore, the weighted QDT provides the lowest measuring error of all linear unbiased frequency estimators. It allows the evaluation of small frequency changes of laser Doppler signals, e.g. from micro-turbulent flows. The theory presented here is verified by Monte Carlo simulations and experiments.

A Flash-ADC data acquisition (DAQ) system has been developed for the drift chamber array designed for the External-Target-Experiment at the Cooling Storage Ring at the Heavy Ion Research Facility, Lanzhou. The simplified readout electronics system has been developed using the Flash-ADC modules and the whole waveform in the sampling window is obtained, with which the time and energy information can be deduced with an offline processing. A digital filter algorithm has been developed to discriminate the noise and the useful signal. With the digital filtering process, the signal to noise ratio (SNR) is increased and a better time and energy resolution can be obtained.