Residual Signal Research Articles

External-cavity diode laser at 2.6 µm and its frequency stabilization with a scanning Fabry-Pérot cavity

We report on the construction of an external cavity diode laser at 2.6 µm wavelength and its frequency stabilization with an external scanning Fabry-Pérot cavity using Pound-Drever-Hall (PDH) frequency stabilization technique. We discuss the frequency noise characteristics of the laser using the PDH residual error signal analysis and the locking performance. We have achieved a 2.5 x 105 suppression factor in the frequency noise power spectral density at Fourier frequencies lower than 100 Hz when the laser is stabilized. The laser is employed to observe resonant excitation on the 5s5p3P0 →5s4d3D1 transition in cold 88Sr atoms trapped in a magneto-optical trap. We have observed an enhancement in the trapped atom number by a factor of 8.3 compared to the case when a single laser at 707 nm is employed for repumping.

NIMG-31. CASE REPORT: DIAGNOSIS OF AN INFILTRATIVE GLIOMA IN A LI-FRAUMENI SYNDROME PATIENT OBSCURED BY LESION RESPONSIVENESS TO NSCLC TREATMENT

Abstract Li-Fraumeni syndrome is a rare, autosomal dominant disorder linked to germline mutations of the TP53 suppressor gene. Patients with this syndrome are predisposed to various malignancies and a variety of brain tumors to include choroid plexus carcinoma, medulloblastoma, and glioma. We present a 46-year-old woman with a history of Li-Fraumeni syndrome which manifested with multiple systemic malignancies including breast cancer, ovarian teratoma, and leiomyosarcoma. The patient had been undergoing MRI brain surveillance since 2018, which was initially notable for a right cerebellar midline focus of enhancement associated with slowly evolving right middle cerebellar peduncle FLAIR signal, with no detectable neurologic symptoms. She underwent systemic therapy for non-small cell lung cancer (NSCLC) with pemetrexed/cisplatin. Her next MRI brain in August 2023 showed resolution of the cerebellar enhancement with small residual FLAIR signal. In April 2024, the patient presented with new neurologic symptoms to include ataxia, dizziness, difficulty swallowing, and memory loss. Repeat MRI brain revealed new infiltrative FLAIR signal throughout the right temporal lobe extending from the posterior fossa, contiguous with the previously seen cerebellar findings and consistent with infiltrative glioma. Due to fragmented records and care in multiple systems, her updated MRI initially created a diagnostic challenge with a radiographic interpretation of likely encephalitis. This is a unique case documenting a middle-aged female with Li-Fraumeni syndrome, followed for multiple cancers, presenting with new neurologic symptoms and progressive infiltrative FLAIR changes spanning from the cerebellum into the right temporal lobe. These findings highlight the natural history of a TP53 deficient tumor that ostensibly responded to a cisplatin-based regimen given for a different indication, thereby delaying time to symptom onset and need for biopsy. Surveillance of low grade lesions in patients with underlying tumor predisposition syndromes must be interpreted in context of systemic disease burden and concurrent treatments for other neoplasms.

Extraction Methods for Brain Biopsy NMR Metabolomics: Balancing Metabolite Stability and Protein Precipitation

Background/Objectives: Metabolic profiling of tissue samples via liquid-state nuclear magnetic resonance (NMR) requires the extraction of polar metabolites in a suitable deuterated solvent. Such methods often prioritise metabolite recovery over protein removal due to the relatively low sensitivity of NMR metabolomics and the routine use of methods able to supress residual protein signals. However, residual protein may impact metabolite integrity and the metabolite stability after NMR sample preparation is often overlooked. This study aimed to investigate the effect of residual protein contamination in rodent brain extracts and identify a reproducible extraction method that optimises metabolite recovery while ensuring sample stability. Methods: The performance of acetonitrile/water (50–100% MeCN), methanol/water (50–100% MeOH), and methanol/water/chloroform (MeOH/H2O/CHCl3) were assessed for extraction efficiency, reproducibility, residual protein contamination, and metabolite stability up to eight hours post NMR sample preparation. Results: Aspartate and glutamate deuteration were observed in 50% MeCN, 50% MeOH, and 67% MeOH extractions along with the conversion of N-acetyl aspartate to aspartate and acetate in 50% MeCN and 50% MeOH extractions. Both observations correlated with residual protein contamination and, thus, are a result of inadequate protein precipitation, as confirmed by ultrafiltration. MeOH/H2O/CHCl3 extraction preserved the stability of these metabolites while maintaining good extraction efficiency and reproducibility. Conclusions: Thus, we recommend MeOH/H2O/CHCl3 extraction for untargeted brain NMR metabolic profiling due to its effective protein precipitation and reliable performance. Nonetheless, the performance of detecting metabolites prone to oxidation such as ascorbate and glutathione is not improved by this method.

Systematic characterization of new EBT4 radiochromic films in clinical x-ray beams

Objective. We aim to characterize kinetics of radiation-induced optical density in newly released EBT4 radiochromic films exposed to clinical x-rays. Several film models and batches were evaluated for the film sensitivity, optical signal increasing with time, relative film noise, and minimum detectable limits (MDL).Approach. Radiochromic film pieces from a single batch of EBT3 and three batches of EBT4 were exposed to doses of 77.38 cGy, 386.92 cGy, and 773.84 cGy using a 6 MV x-ray beam. The films were scanned with a flatbed scanner at specific time intervals up to 120 h. The time-series net optical density of red, green and blue colors was corrected for response of the scanner with time and studied to establish the saturation characteristics of film polymerization process. Dose-response from 3.86 cGy to 1935 cGy was also determined for each color. MDL of the films was quantitatively defined as the dose that would double the net optical density of red color above the standard deviation of the residual signal at zero dose. The relative noise characteristics of EBT3 versus EBT4 were studied as a function of time, dose and scanner resolution.Main Results. For doses ≥ 100 cGy, analysis revealed a stability of optical density beyond 48 h post-exposure for EBT3 and EBT4 films. EBT3 films attained 80%-90% of their net optical density at 48 h within minutes of irradiation, compared to 72%-88% for EBT4 films. The rate of growth was slowest for blue color, fastest for red, while green was in between the two. The MDL for EBT4 averaged 15 cGy for three batches, whereas EBT3 films reliably detected doses as low as 8.5 cGy.Significance. Several batches of the new EBT4 film showed slightly lower response compared to its predecessor over 3.86 cGy to 1935 Gy range. For all practical purposes, the post-irradiation growth of polymers ceases between 48 to 60 h for both EBT films. Overall, the EBT4 film exhibited noise characteristics similar to EBT3, except for lower doses where the noise was observed to be higher than its predecessor.

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression inSimultaneous Slices.

Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely nonneural, appearing stronger in neurovasculature than gray matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting-state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods.

Research on detection and protection technology of DC residual current

Abstract To realize that the residual current device can accurately and quickly detect the fault when the fluctuating DC residual current occurs in the system and improve the system’s safety, a DC residual current detection and protection method based on VMD was proposed. Particle Swarm Optimization (PSO) was used to optimize the penalty factor and the number of decomposition layers of VMD. The minimum envelope entropy of the residual current signal is used as the fitness function to obtain the optimal combination. The criterion of fault occurrence is established based on the optimal modal component mutation characteristic of the residual current decomposed by VMD. Experiments show that this method can identify faults quickly and effectively when pulsating DC residual current is generated in the system.

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.

Rapid and quantitative CEST-MRI sequence using water presaturation.

Despite the significant potential for in vivo metabolic imaging in preclinical and clinical applications, CEST MRI suffers from long scan time and inaccurate quantification. This study aims to suppress the contaminations among signals under different frequencies, which could shorten the TR and thereby facilitate CEST imaging acceleration and quantification. A novel sequence is proposed by applying a water-presaturation (WPS) module at the beginning of each TR. WPS CEST quickly knocks down the residual signal from previous TRs so that the magnetization of all TRs recovers from zero, which aligns well with the formula of quasi-steady-state theorem and enables accurate quantification within shorter TR. WPS CEST was assessed by simulations, creatine phantom, and healthy human brain scans at 3 T. In simulation and phantom experiment, WPS CEST allows accurate estimation of exchange rate (ksw) using omega plot and using shorter delay time (Td) and saturation time (Ts) (e.g., 1 s/1 s) compared with the conventional CEST. Simulations further showed that WPS CEST could obtain consistent spin-lock relaxation (R1ρ) values over varied Tds and Tss. Six human scans indicated that R1ρ collected from conventional sequences showed significant differences between two groups with Td and Ts of (1 s/1 s) and (2 s/2 s) (amide: 1.721 ± 0.051 s-1 vs. 1.622 ± 0.050 s-1, p = 0.001; nuclear Overhauser enhancement: 1.792 ± 0.046 s-1 vs. 1.687 ± 0.053 s-1, p = 0.004), whereas WPS CEST scans using these 2 Td/Ts values obtained the same mean R1ρ (amide: 1.616 ± 0.053 s-1 vs. 1.616 ± 0.048 s-1, p = 0.862; nuclear Overhauser enhancement: 1.688 ± 0.064 s-1 vs. 1.684 ± 0.054 s-1, p = 0.544). WPS CEST demonstrated accurate quantitation within shorter TR compared with conventional sequences, and thereby may allow rapid quantitative CEST scans in various situations.

A Modified Bleaching Method for Multiplex Immunofluorescence Staining of FFPE Tissue Sections.

Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.

A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation.

To present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues. Electrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies. We conducted intracranial physiology studies on five consecutive patients with Parkinson's disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials. After artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite. The proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.

7405 A Case of Pituitary Apoplexy in Pregnancy

Abstract Disclosure: Y. Esaki: None. G.E. Vates: None. I. Shafiq: None. Pituitary apoplexy is a rare cause of headache during pregnancy. The prevalence of pituitary apoplexy during gestation and the peripartum period is estimated to be 1 in 10,000 term pregnancies. Approximately 42-47% of these patients were known to have a pituitary lesion especially history of prolactinoma. Here we report a pregnant female developing pituitary apoplexy after discontinuation of dopamine agonist. A 27-year-old female was initially diagnosed with prolactinoma with elevated prolactin of 158.1 ng/ml (3.0-300 ng/ml). MRI revealed a 1.3 cm x 1.5 cm x 1.4 cm pituitary mass with solid and cystic components. Treatment with cabergoline 0.25 mg twice weekly was started. After 2 years of treatment, prolactin was 74.2 ng/ml and the size of the adenoma decreased to 1.0 cm x 1.2 cm x 1.3 cm. One year later, she became pregnant at age 30 and discontinued cabergoline after 7 weeks of gestation. At 12 weeks of gestation, she presented to the Emergency Department for 3 days of severe headaches associated with emesis. The ED work up includes prolactin > 2,000 ng/ml. MRI head without contrast demonstrated an interval increase in size to 1.5 cm x 1.7 cm with hemorrhagic component. Urgent evaluation by a Neuro-ophthalmologist revealed no clinical evidence of optic neuropathy. Blood work showed AM cortisol 2.3 mcg/dl (4.0-22.0 mcg/dl), TSH 0.5 mIU/L (0.4-0.5 mIU/L), FT4 0.6 ng/dl (0.8-1.8 ng/dl). Cabergoline 0.25 mg twice weekly was restarted to control the size of the tumor as well as hydrocortisone and levothyroxine for central adrenal insufficiency and hypothyroidism respectively. Four weeks later, she was 17 weeks pregnant. MRI showed a further increase in the size of the adenoma, now measuring 1.8 cm x 2.0 cm x 1.3 cm. The lesion now extended into the suprasellar cistern, affecting the optic chiasm. She underwent the transsphenoidal resection of the pituitary adenoma at 18 weeks of gestation due to the increase in size of the tumor. The final pathology was consistent with pituitary adenoma, lactotroph cell type, WHO grade 1. MRI obtained 3 months post-operation showed no residual T1 hyperintense tumor signal. There was a cyst-like expansion of the sella turcica with a decreased mass effect on the optic chiasm. She delivered her healthy baby at 40 weeks and 5 days via vaginal delivery. She received stress dose steroid during labor, then was discharged home with hydrocortisone 10 mg twice daily. She is doing well following the delivery and breastfeeding the baby. This case illustrates a rare case of pituitary apoplexy during pregnancy in a patient with prolactinoma. Pituitary apoplexy should be considered as a differential diagnosis when pregnant patients develop an acute, severe headache, especially those with an underlying pituitary adenoma. It is critical to assess and treat the hormonal deficiencies. For prolactinoma, pharmacological therapy can be initiated. Surgery is considered if the medical therapy fails. Presentation: 6/3/2024

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data

Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies. While various residual motion nuisance regressors have been proposed to regress out residual motion artifact after motion correction, these validations have typically been conducted empirically in in vivo data, since realistic head motion–corrupted MR data are not available. Here, we generated motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom using the simulated prospective acquisition correction (SIMPACE) sequence. Testing SIMPACE motion-corrupted data with various intervolume motion patterns, we first investigated the mechanism of the residual motion signal after motion correction and also proposed a voxel-wise motion nuisance regressor, called the partial volume (PV) regressor. We also modified the slice-oriented motion-correction method (SLOMOCO) pipeline with 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and the proposed PV motion nuisance regressor. We then compared the residual signal after application of the modified SLOMOCO (mSLOMOCO) pipeline with two other methods: intervolume motion-correction method (VOLMOCO), and the original SLOMOCO (oSLOMOCO). We found that mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors outperformed both VOLMOCO with 6 Vol-mopa and PV regressors and oSLOMOCO with 14 voxel-wise regressors. In tests of the 10 different motion patterns of SIMPACE datasets with 1× and 2× amplified intravolume motion, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline produced the average standard deviation (SD) of the residual time series signals in the gray matter (GM) smaller by 29% (1× amplified intravolume motion) and 45% (2× amplified intravolume motion) than VOLMOCO with 6 Vol-mopa and PV regressors pipeline. Also, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline outperformed oSLOMOCO with 14 voxelwise regressors pipeline, generating the average SD in GM smaller by 28% (1× amplified intravolume motion) and 31% (2× amplified intravolume motion) than oSLOMOCO with 14 voxel-wise regressors pipeline. The novel PV regressor also effectively reduced residual motion artifact as a motion nuisance regressor after both VOLMOCO and mSLOMOCO.

Improved event-based fault detection filter for networked fuzzy systems under DoS attacks

This paper investigates an improved event-based fault detection method for networked fuzzy systems under denial-of-service (DoS) attacks. In order to solve the bandwidth occupation problem of communication network, a resilient event-triggered transmission strategy is developed. Additionally, a fault detection filter is designed to estimate the time of fault occurrence by using the residual signal. Under this framework, a novel Lyapunov functional related to attack parameters is established to analyze the exponential convergence of the error signals, and the filter gains and event-triggered parameters are obtained by solving linear matrix inequalities. The designed functional reduces the conservatism of the stability criteria significantly in contrast with the previous discontinuous Lyapunov functionals. Finally, a simulation example is provided to verify the effectiveness of the proposed method.

An improved Takagi–Sugeon–Kang-based FxLMS algorithm and its active noise control in electric bus driving position

This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.

Residual and Unmodeled Ocean Tide Signal From 20+ Years of GRACE and GRACE‐FO Global Gravity Field Models

AbstractWe analyze remaining ocean tide signal in K/Ka‐band range‐rate (RR) postfit residuals, obtained after estimation of monthly gravity field solutions from 21.5 years of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On sensor data. Low‐pass filtered and numerically differentiated residuals are assigned to grids and a spectral analysis is performed using Lomb‐Scargle periodograms. We identified enhanced amplitudes at over 30 ocean tide periods. Spectral replicas revealed several tides from sub‐semidiurnal bands. Increased ocean tide amplitudes are located in expected regions, that is, in high‐latitude, coastal and shallow water regions, although some tides also show distinct patterns over the open ocean. While most identified tides are considered during processing, and therefore the amplitudes represent residual signal w.r.t. the ocean tide model, several unmodeled tides were found, including astronomical degree‐3 tides , , , , and radiational and/or compound tides , , , and . The astronomical degree‐3 tides were observed on a global level for the first time a few years ago in altimeter data. We are unaware of any global data‐constrained solutions for the other tides. The amplitude patterns of these tides exhibit similarities to purely hydrodynamic solutions, and altimeter observations (astronomical degree‐3 only). The sensitivity of the satellites to these rather small tidal effects demands their inclusion into the gravity field recovery processing to reduce orbit modeling errors and a possible aliasing. The conducted study shows enormous potential of RR postfit residuals analysis for validating ocean tide models and improving gravity field recovery processing strategies.

The mean scatterer spacing estimation with the Gabor atoms selected based on the Nakagami distribution

Abstract Mean scatterer spacing (MSS) is a quantitative signature for disease diagnosis and tissue characterization. However, it is difficult to objectively extract the coherent components from the ultrasound RF echo signals to improve the accuracy performance of the MSS estimation. In the present study, a novel MSS estimation method with the matched Gabor atoms selected based on Nakagami parameters is proposed. Firstly, the ultrasound signals are decomposed into a series of Gabor atoms, and the envelopes of the residual signals are fitted with the Nakagami distribution. Then, the second order difference (SOD) of the shape parameters in Nakagami distribution is calculated to select the optimal atoms to match the coherent components. Finally, the locations of the selected Gabor atoms are used to estimate the MSS. In order to evaluate the performance of the proposed method, the simulated RF echo signals are modelled based on four regular degrees of the scatterer distributions, and then the optimally matched atoms are selected to estimate MSSs. The results based on simulated signals demonstrate that the propose method can provide accurate MSS estimation, which are advantageous for improving quantitative diagnosis of diseases and tissue characterization

Model‐based fault diagnosis for safety‐critical chemical reactors: An experimental study

AbstractThis article presents an experimental application of fault detection, isolation, and estimation in a chemical reactor system, introducing a functional observer‐based approach without the need for linear approximation. The residual signal generators, functioning as disturbance‐decoupled functional observers, provide fault size estimates and enable fault isolation through multiple generators operating independently. The experimental study focuses on the 3‐Picoline oxidation process, deriving a discrete‐time model, and constructing specific residual generators for coolant inlet temperature and feed concentration faults. Fault diagnosis employs Fast Fourier Transform (FFT) filtering and Generalized Likelihood Ratio (GLR), facilitating on‐the‐fly detection during the experiment. The effectiveness of fault detection, disturbance decoupling, and estimation is experimentally validated.

Fault detection for saturated nonlinear system subject to redundant channel and additive time‐varying delays

AbstractUnder the influence of sensor saturation and packet dropout, this paper is concerned with the fault detection (FD) problem for a class of nonlinear systems with two additive time‐varying delay components. In the addressed measurement model, the sensor is assumed to have two communication channels, which can contribute to improving the probability of successfully delivering the measurements. A FD filter is designed to generate residual signals, and the sufficient criteria are established to ensure the stochastic finite‐time stability (SFTS) of the residual system with performance constraint by constructing new Lyapunov‐Krasovskii functionals. Based on the established criteria, the explicit expression of the desired FD filter is obtained via solving a set of linear matrix inequalities (LMIs). Finally, the usefulness of the proposed FD scheme is verified by a simulation example.