Amplitude Variance Research Articles

Diagnostic Value of Bereitschaftspotential in People With Functional Seizures.

Bereitschaftspotential (BP) or readiness potential in people with functional movement disorders can aid diagnostic workup. We evaluated the diagnostic value of BP as an interictal EEG marker in people with functional seizures (FS). We recorded and analyzed BP interictal before intended movements in 17 adults with FS and 17 controls with alternative diagnoses. We evaluated the signals for the presence of BP, latency, amplitude, and early versus late BP. Bereitschaftspotential was present in all except one person with FS. We found no significant differences in the latency and amplitude of BP between participants with FS and controls. The early BP showed the most significant variance in amplitude, latency, and presence. We found interictal typical BP values in participants with FS and variable semiology, while earlier research found interictal no BP in functional movement disorders. These findings do not support the use of BP as an interictal diagnostic tool for FS. Differences in early BP and focus on FS with pure motor semiology are starting points for further research evaluating potential interictal markers in people with FS.

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations.

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data.

Stroke rehabilitation interventions require multiple training sessions and repeated assessments to evaluate the improvements from training. Biofeedback-based treadmill training often involves 10 or more sessions to determine its effectiveness. The training and assessment process incurs time, labor, and cost to determine whether the training produces positive outcomes. Predicting the effectiveness of gait training based on baseline minimum foot clearance (MFC) data would be highly beneficial, potentially saving resources, costs, and patient time. This work proposes novel features using the Short-term Fourier Transform (STFT)-based magnitude spectrum of MFC data to predict the effectiveness of biofeedback training. This approach enables tracking non-stationary dynamics and capturing stride-to-stride MFC value fluctuations, providing a compact representation for efficient processing compared to time-domain analysis alone. The proposed STFT-based features outperform existing wavelet, histogram, and Poincaré-based features with a maximum accuracy of 95%, F1 score of 96%, sensitivity of 93.33% and specificity of 100%. The proposed features are also statistically significant (p 0.001) compared to the descriptive statistical features extracted from the MFC series and the tone and entropy features extracted from the MFC percentage index series. The study found that short-term spectral components and the windowed mean value (DC value) possess predictive capabilities regarding the success of biofeedback training. The higher spectral amplitude and lower variance in the lower frequency zone indicate lower chances of improvement, while the lower spectral amplitude and higher variance indicate higher chances of improvement.

Data-driven prediction of rail neutral temperature for continuously welded rails using impulse-based vibration frequencies

Continuously welded rails (CWR) are prone to the development of high thermal-induced load along the axial direction. Excessive levels of load lead to risk of rail buckling and potential for derailment. Knowledge of the in situ rail axial load in CWRs is therefore important to ensure safe rail management. Field-deployable, nondestructive evaluation techniques for measuring the rail load, or a widely adopted alternative called rail neutral temperature (RNT), are desired. This study uses a data-driven approach to investigate if rail dynamic response data, collected in a non-destructive fashion, can be used to predict RNT. The study is based on a data set comprising rail equivalent strain, temperature and vibration resonance frequencies that was collected from a revenue-service rail over a period of nearly two years. All excited vibration resonance peaks are identified from other peaks caused by noise using spectral amplitude variance. Among these resonance peaks, potentially useful resonances are identified with respect to stacked spectra collected across a testing day using an assumed temperature-frequency relation. A subset of the identified useful resonances is then identified based on their consistent appearance across both testing locations and all testing days, strong correlation to effective strain, and strong correlation to each other. Three particular vibration resonances (or vibration modes -- these terms will be used interchangeably throughout this paper unless specified otherwise. The term mode does not necessarily indicate mode shapes or mode families.) emerge from this process as best candidates. A classic feature selection technique, Lasso linear regression, is then employed to identify critical power combinations of the three resonant mode frequencies. Two power combinations exhibit unique correlation to the measured equivalent axial strain at both test locations across all testing days, and thus show particular ability to predict RNT. The RNT is predicted at one test location using different models based on the power combination data from the other location, and vice versa, where the predictions satisfy standard RNT measurement accuracy expectations.

Intelligence identification of tunnel surrounding rock mass grades using drilling jumbo data

In drill-and-blast tunnels, the recorded data from fully computerized rock drilling jumbos can reflect the tunnel face rock mass conditions. An intelligent method based on real-time drilling data was developed herein to identify tunnel rock mass grades. First, the time series of 14 drilling parameters was processed using a Fourier transform, and the maximum amplitudes of each parameter were computed. Subsequently, the means, medians, and variances of the parameter amplitudes from different blast holes on the same tunnel face were satisfied to form the original feature set. Neighborhood component analysis was used to select the key rock mass condition features. From this feature selection (FS), the top five features were selected as the final feature set. Furthermore, using the selected features, a Gaussian Process Classification (GPC)-based model was established to predict rock mass grades. The proposed method captures both the drilling data information in the time dimension and the inherent connection of different blast hole data, making it advantageous. Using field construction data from Xiwuling Tunnel, the proposed FS-GPC model performance was evaluated. It achieved an accuracy of 90% in predicting rock mass grades. The proposed method can accurately and rapidly predict rock mass grades in drill-and-blast tunnels, optimizing engineering design and construction decisions.

Investigation of Lateral Vibrations in Turbine-generator Unit 5 of the Inga 2 Hydroelectric Power Plant

The article presents a case study on the Investigation of lateral vibrations in the turbine-generator unit 5 of the Inga 2 hydroelectric power plant in the Democratic Republic of the Congo. Lateral vibrations were experimentally determined using twelve proximity and eddy-current probes, positioned on each measurement plane. The results were analyzed using the Dasylab and R software. Hence, it was observed that the vibration amplitudes of the upper guide bearing, lower guide bearing, and pivot/rotor exceeded acceptable or critical limit values of the international vibration standard for a rotating speed between 100-250 rpm. These excesses can lead to rotor mass imbalances, the eccentricity of the rotor axis relative to the rotation axis of the shaft, and the deformation of the coupling shaft between the upper rotor shaft and the turbine rotor shaft. Subsequently, the means and the variances of the vibration amplitudes were evaluated and compared to the reference values of the international standard. The results of the compliance analysis revealed statistically significant differences between the measured amplitudes and the reference values. Thus, it indicates deviations from international specifications.

Local maximum instantaneous extraction transform based on extended autocorrelation function for bearing fault diagnosis

Extracting weak impulses from vibration signals is a tricky technique in fault diagnosis and condition monitoring of the rotating machinery. The autocorrelation function is an effective method for enhancing repetitive signals, however, the effect deteriorates as the point number of autocorrelation calculation decreases. To resolve this problem, a novel algorithm entitled extended autocorrelation function is presented. Besides, an algorithm called local maximum instantaneous extraction transform is proposed to further improve the enhancement effect, combining short-time Fourier transform, gamma transform, and local maximum sampling. Therefore, the presented algorithm is called the local maximum instantaneous extraction transform based on the extended autocorrelation function (LMIET-EACF). Firstly, we discuss the mathematical statistics theory of the autocorrelation function for realizing signal enhancement. It is illustrated that, when the number of data points involved in the calculation decreases, the amplitude variances of the noise components will increase. This is the limitation of the autocorrelation function. Secondly, an extension operation is conducted in the extended autocorrelation function to make the variance constant by keeping the number of calculation points constant. This operation can improve the enhancement effect for the fault impulses. Furtherly, the short-time Fourier transform is employed to suppress the noise. The contrast of the time–frequency distribution is improved by the gamma transform. And the local maximum sampling is employed to extract the fault impulse peaks to improve the time–frequency concentration. These operations constitute the local maximum instantaneous extraction transform. The proposed method is validated by a series of simulated signals and two public experimental signals. And a comparison is done between the LMIET-EACF and other mainstream signal enhancement algorithms. The results indicate that the proposed method performs better than several common time–frequency distributions, by quantitatively evaluating with the Rényi entropy. In addition, it can catch the fault signal in a lower signal-to-noise ratio, compared with some novel post-processing methods.

Predicting Wood Density Using Resistance Drilling: The Effect of Varying Feed Speed and RPM

The IML PD series Resi is a device used to assess the drilling resistance of wood. The IML PD series Resi instrument is being widely adopted for commercial wood quality assessment due to its speed, cost-effectiveness, and precision when combined with web-based trace processing. Collecting Resi data with fixed feed speed and RPM settings is challenging due to inherent basic density variations within and between tree species. Altering these settings affects the drilling resistance amplitude of the Resi data, impacting basic density predictions. This study introduces the concept of chip thickness to combine feed speed and RPM into a single parameter to minimise the effects of different sampling conditions on the basic density predictions. Regression models, with chip thickness as the regressor variable, account for 97% to 99% of variance in mean Resi outerwood amplitude across six species. The demonstrated adaptability of chip thickness for adjusting feed speed and RPM settings, along with species-specific functions correlating it with Resi amplitude, holds promise for standardizing amplitude values across diverse feed speeds and RPM settings. Optimal sampling conditions needed to predict basic density lie within the 30%–40% amplitude range. To drill a ~30 cm diameter tree, the recommended fastest settings were 200 cm/min and 3500 RPM for Southern Pine (Pinus elliottii var. elliottii (Engelm) × Pinus caribaea var. hondurensis (Sénéclauze)) and Radiata Pine (Pinus radiata (D. Don.)), 200 cm/min and 2500 RPM for Hoop Pine (Araucaria cunninghamii (Mudie)), 50 cm/min and 5000 RPM for Spotted Gum (Corymbia citriodora subsp. variegata (F. Muell.)), 200 cm/min and 4500 RPM for White Cypress (Callitris glaucophylla (Thompson & Johnson)), and 150 cm/min and 3500 RPM for Shining Gum (Eucalyptus nitens (H. Deane & Maiden) Maiden) based on the billets sampled.

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images.

The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels' amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel's probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

Study on dynamic characteristics of scraper conveyor under various scraper configurations

This study aims to explore the impact of scraper structure on the stable operation of scraper conveyors. A simulation test is designed to investigate the effect of the scraper's lower inclination angle on the wedge-shaped structure of the scraper. Based on the test results, modifications are made to the scraper's lower inclination angle. Using the DEM-MBD coupling model of the scraper conveyor, simulation analyses are conducted to evaluate the influence of the scraper on the dynamic characteristics of the conveyor before and after the modification. The simulation test results reveal that, when coal materials are stuck, the minimum impact force of coal material on the upper sample is observed at inclination angles of 52.35° and 78.19°. The simulation research indicates that the transportation efficiency of the standard scraper and the single-sided 52.35° and 78.19° modified scraper is similar. The single-sided 78.19° scraper demonstrates the most stable vibration amplitude and centroid variance, with an overall maximum vibration amplitude and variance of 0.76 mm and 0.08, respectively. The z-direction speed fluctuation of the single-sided 78.19° scraper is also the smallest and most stable, with a maximum peak-valley difference and fluctuation variance of 0.17 m/s and 0.0004, respectively. Additionally, the force of the coal on the single-sided 78.19° scraper is the smallest and relatively stable, with a fluctuation range of 510.28∼ 18,557.71 N. Overall, the dynamic operation effect of the single-sided 78.19° scraper is deemed the best after considering all factors. This study provides valuable insights into the influence of scraper structure with different lower inclination angles on the stable operation of scraper conveyors and offers a reference for optimizing scraper structure.

On the Mathematical Relationship Between Contextual Probability and N400 Amplitude.

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty. Specifically, we use 37 contemporary transformer language models to calculate the contextual probability of stimuli from 6 experimental studies of the N400, and test whether N400 amplitude is best predicted by a linear, logarithmic, super-logarithmic, or sub-logarithmic transformation of the probabilities calculated using these language models, as well as combinations of these transformed metrics. We replicate the finding that on some datasets, a combination of linearly and logarithmically-transformed probability can predict N400 amplitude better than either metric alone. In addition, we find that overall, the best single predictor of N400 amplitude is sub-logarithmically-transformed probability, which for almost all language models and datasets explains all the variance in N400 amplitude otherwise explained by the linear and logarithmic transformations. This is a novel finding that is not predicted by any current theoretical accounts, and thus one that we argue is likely to play an important role in increasing our understanding of how the statistical regularities of language impact language comprehension.

Exploring the characteristics of folk music tunes based on data mining model

Abstract In this paper, during the design process, some sample data are first extracted, and the distance between data is calculated to obtain the symmetric distance matrix along the diagonal. The model mutual information quantity is introduced, and the homogenization factor is detected after weight calculation. Next, analyze the rule weights based on the normalized mutual information quantity and use information entropy to identify abnormal data. Finally, find the minimum set with frequent item set and mine the fuzzy association rules. To verify the feasibility of the model, the results of exploring the characteristics of ethnic music tunes show that the neutral tone color is thin and the ethnic music melody line is soft in tune when the long and short melody lines are 9% and 12%, respectively. The number of strong and weak beats in the beat is 2-7 times, mixed with 2/4 and 3/8 beats alternately, and the musical tune is regular. The third-order interaction of wave amplitude variance is significant, and the two media of CD and phonograph help listeners understand and remember the music. Based on this, the digital model can improve the reproduction effect of folk music and can objectively target the transmission of folk music with national cultural characteristics.

No evidence for differential saccadic adaptation in children and adults with an autism spectrum diagnosis.

Altered patterns of eye-movements during scene exploration, and atypical gaze preferences in social settings, have long been noted as features of the Autism phenotype. While these are typically attributed to differences in social engagement and interests (e.g., preferences for inanimate objects over face stimuli), there are also reports of differential saccade measures to non-social stimuli, raising the possibility that fundamental differences in visuo-sensorimotor processing may be at play. Here, we tested the plasticity of the eye-movement system using a classic saccade-adaptation paradigm to assess whether individuals with ASD make typical adjustments to their eye-movements in response to experimentally introduced errors. Saccade adaptation can be measured in infants as young as 10 months, raising the possibility that such measures could be useful as early neuro-markers of ASD risk. Saccade amplitudes were measured while children and adults with ASD (N = 41) and age-matched typically developing (TD) individuals (N = 68) made rapid eye-movements to peripherally presented targets. During adaptation trials, the target was relocated from 20-degrees to 15-degrees from fixation once a saccade to the original target location was initiated, a manipulation that leads to systematic reduction in saccade amplitudes in typical observers. Neither children nor adults with ASD showed any differences relative to TD peers in their abilities to appropriately adapt saccades in the face of persistently introduced errors. Of the three studies to date of saccade adaptation in ASD, none have shown deficits in saccade adaptation that are sufficient to generalize to the whole or a subgroup of the ASD population. Unlike prior studies, we found no evidence for a slower adaptation rate during the early adaptation phase, and no of evidence greater variance of saccade amplitudes in ASD. In post hoc analysis, there was evidence for larger primary saccades to non-adapted targets, a finding requiring replication in future work.

Higher-order statistical moments of predicted sonic boom waveforms through turbulence

Sonic booms generated by supersonic aircraft are affected by turbulence in the atmospheric boundary layer through which they propagate. Turbulence effects lead to random variability of the sonic boom waveforms measured on the ground, complicating the prediction of such waveforms. As an initial effort to predict the waveform variability, the solution of the coherent or mean sonic boom waveform has previously been formulated by the author. The current paper extends the formulation to derive an expression of the second-order statistical moment necessary to calculate the variance of the spectral amplitudes. Since the derivation uses a full wave equation, results using the derived expression will be compared with those obtained using a parabolic approximation. In order to fully quantify the uncertainty of our predictions, the higher order statistical moments are also formulated and are shown to be approximately zero. Consequently, the probability density function (pdf) of the spectral amplitudes is predicted to be Gaussian. The formulation is further extended to determine the pdf of the loudness of sonic booms and quantify the uncertainties associated with the loudness prediction. Results from the formulation are compared with available flight test data.

Particle Behavior and Aperture Optimization of Variable Vibration-Amplitude Screening Based on Discrete Element Method Simulation.

Recently, some novel screens were proposed based on the improved understanding of screening processes from particle-scale studies, one of which is the novel variable amplitude equal thickness vibration screen (VAETVS). This paper presents an investigation of using different aperture sizes to further optimize the VAETVS by the discrete element method (DEM). The screen was divided into three panels, and the aperture size of each panel was varied in the simulation. The particles' dynamics and spatial distribution were investigated to better understand the effects of varied amplitudes and aperture sizes on the screening efficiency. The results showed that the VAETVS can be effectively optimized by varied amplitude as well as varied aperture size for different screen panels. The amplitude variance is more effective for hard sieve particles and hindrance particles. It is also found that from the feed end to the discharge end, the difference in the distribution of particle mass between the coarse particles and the fine particles increases. Generally, such a difference can be increased with increasing aperture sizes. However, the effects of the apertures of the different panels were not the same. The simulated data were further analyzed by the Box-Behnken response surface method, which gave a mathematical model to predict the screening efficiency η with the varied apertures. From the model, the optimal aperture sizes are 7.5, 7.58, and 6.81 mm, yielding the maximum screening efficiency of 86.35%, and the optimal opening areas for the three panels are 35, 40, and 40%, respectively, yielding the maximum screening efficiency of 87.82%.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis.

Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

Exploration of muscle–tendon biomechanics one year after Achilles tendon rupture and the compensatory role of flexor hallucis longus

Achilles tendon (AT) rupture leads to long-term structural and functional impairments. Currently, the predictors of good recovery after rupture are poorly known. Thus, we aimed to explore the interconnections between structural, mechanical, and neuromuscular parameters and their associations with factors that could explain good recovery in patients with non-surgically treated AT rupture. A total of 35 patients with unilateral rupture (6 females) participated in this study. Muscle-tendon structural, mechanical, and neuromuscular parameters were measured 1-year after rupture. Interconnections between the inter-limb differences (Δ) were explored using partial correlations, followed by multivariable linear regression to find associations between the measured factors and the following markers that indicate good recovery: 1) tendon length, 2) tendon non-uniform displacement, and 3) flexor hallucis longus (FHL) normalized EMG amplitude difference between limbs. Δmedial gastrocnemius (MG) (β = −0.12, p = 0.007) and Δlateral gastrocnemius (β = −0.086, p = 0.030) subtendon lengths were associated with MG tendon Δstiffness. MG (β = 11.56, p = 0.003) and soleus (β = 2.18, p = 0.040) Δsubtendon lengths explained 48 % of variance in FHL EMG amplitude. Regression models for tendon length and non-uniform displacement were not significant. Smaller inter-limb differences in Achilles subtendon lengths were associated with smaller differences in the AT stiffness between limbs, and a smaller contribution of FHL muscle to the plantarflexion torque. In the injured limb, the increased contribution of FHL appears to partially counteract a smaller contribution from MG due to the elongated tendon, however the role of FHL should not be emphasized during rehabilitation to allow recovery of the TS muscles.

GNSS Data/Pilot Combining with Extended Integrations for Carrier Tracking.

Modern Global Navigation Satellite System (GNSS) signals are usually made of two components: a pilot and a data channel. The former is adopted to extend the integration time and improve receiver sensitivity, whereas the latter is used for data dissemination. Combining the two channels allows one to fully exploit the transmitted power and further improve receiver performance. The presence of data symbols in the data channel, however, limits the integration time in the combining process. When a pure data channel is considered, the integration time can be extended using a squaring operation, which removes the data symbols without affecting phase information. In this paper, Maximum Likelihood (ML) estimation is used to derive the optimal data-pilot combining strategy and extend the integration time beyond the data symbol duration. In this way, a generalized correlator is obtained as the linear combination of the pilot and data components. The data component is multiplied by a non-linear term, which compensates for the presence of data bits. Under weak signal conditions, this multiplication leads to a form of squaring, which generalizes the squaring correlator used in data-only processing. The weights of the combination depend on the signal amplitude and noise variance that need to be estimated. The ML solution is integrated into a Phase Lock Loop (PLL) and used to process GNSS signals with data and pilot components. The proposed algorithm and its performance are characterized from a theoretical point of view, using semi-analytic simulations and through the processing of GNSS signals generated using a hardware simulator. The derived method is compared with other data/pilot combining strategies with extended integrations showing the advantages and drawbacks of the different approaches.

Yield Adjustment Using GPR-Derived Spatial Covariance Structure in Cassava Field: A Preliminary Investigation

Many processes concerning below-ground plant performance are not fully understood, such as spatial and temporal dynamics and their relation to environmental factors. Accounting for these spatial patterns is very important as they may be used to adjust for the estimation of cassava fresh root yield masked by field heterogeneity. The yield of cassava is an important characteristic that every breeder seeks to maintain in their germplasm. Ground-Penetrating Radar (GPR) has proven to be an effective tool for studying the below-ground characteristics of developing plants, but it has not yet been explored with respect to its utility in normalizing spatial heterogeneity in agricultural field experiments. In this study, the use of GPR for this purpose was evaluated in a cassava field trial conducted in Momil, Colombia. Using the signal amplitude of the GPR radargram from each field plot, we constructed a spatial plot error structure using the variance of the signal amplitude and developed GPR-based autoregressive (AR) models for fresh root yield adjustment. The comparison of the models was based on the average standard error (SE) of the Best Linear Unbiased Estimator (BLUE) and through majority voting (MV) with respect to the SE of the genotype across the models. Our results show that the GPR-based AR model outperformed the other models, yielding an SE of 9.57 and an MV score of 88.33%, while the AR1 × AR1 and IID models had SEs of 10.15 and 10.56% and MV scores of 17.37 and 0.00%, respectively. Our results suggest that GPR can serve a dual purpose in non-destructive yield estimation and field spatial heterogeneity normalization in global root and tuber crop programs, presenting a great potential for adoption in many applications.

Variational Bayesian Inference Time Delay Estimation for Passive Sonars

In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sampling rate). When a true time delay is out of the grid, the estimation accuracy deteriorates due to the mismatch between the real-time delay and the discrete grid. This paper proposes a new method for time delay estimation, which realizes the time delay estimation under the framework of variational Bayesian inference. The proposed method is grid-less, that is, continuous in the time domain. Unlike the popular grid-less compressive time delay estimation method, this method does not require parameter adjustment, and can automatically estimate the number of time delays, noise variance, and amplitude variance. The simulation results showed that the performance of the proposed method was superior to the reference state-of-the-art time delay estimation methods.