Pseudo Signal Research Articles

Pseudo-ISP: Learning pseudo in-camera signal processing pipeline from a color image denoiser

Existing deep denoisers usually ignore the problem of noise discrepancy between training and test images caused by the different noise modeling of sensor and in-camera signal processing (ISP) pipeline, which inevitably degrades the denoising performance. In this paper, we present an unpaired learning scheme to adapt the color image denoisers for handling test images with noise discrepancy. To this end, we consider a practical training setting equipped with a pre-trained color denoiser, a set of test noisy sRGB images, and an unpaired set of clean sRGB images. Then, we propose to alternate between two modules, i.e., Pseudo-ISP training for learning noise modeling of sensor and in-camera signal processing (ISP) pipeline and denoiser adaption. Instead of modeling the complex noise in sRGB images, we assume the signal-dependent and spatially independent noise in rawRGB space, and specifically design the Pseudo-ISP to learn the pseudo ISP pipeline and pseudo rawRGB noise model jointly. Applying the learned Pseudo-ISP to the unpaired set of clean sRGB images, the corresponding realistic noisy sRGB observations can be easily obtained. Conversely, using the generated paired data for model fine-tuning, the color denoiser can be well adapted for test images. Through iterating between Pseudo-ISP training and denoiser adaption, denoising performance of deep denoisers can be further improved. Experiments show that our Pseudo-ISP not only can boost simple Gaussian blurring-based denoiser to achieve competitive performance against CBDNet, but also is effective in improving state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.

COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training.

Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we systematically investigate the partial-label segmentation problem with theoretical and empirical analyses on the prior techniques. We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on one public and three private partial-label segmentation tasks over 12 CT datasets. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data sizes.

Proposal for Generation of a Pseudo GPS Signal Used in Indoor Mode for Navigation Testing of Small Autonomous Flying Robot

In addition to the inertial navigation sensors embedded in an autonomous flying robot, Global Navigation Satellite Systems are most often used, in particular, the GPS (Global Positioning System). This offer information with an accuracy that depends on the arrangement of a constellation of satellites. It is very often that a measurement with errors of several meters is delivered by this system. With the need for agile decisions of the autonomous flying robot during its mission, especially in the case of rescuing people during disasters, it is not allowed for uncertainty to reign over the decision of the flying robot. In order to improve the estimators implemented experimentally, a secure laboratory test environment is more preferable. Here, we propose a way that helps researchers to conduct indoor experiments with a pseudo GPS that offers circumstances similar to outdoor navigation. The results that will be discussed are obtained with the Vicon motion tracker laboratory at Aalborg University, Denmark.

Research on the Enhancement Method of Specific Emitter Open Set Recognition

Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.

Atrial fibrillation detection with signal decomposition and dilated residual neural network

Objective. Detecting atrial fibrillation (AF) using electrocardiogram (ECG) recordings from wearable devices has been challenging due to factors such as low signal-to-noise ratio and the use of only one lead. The use of deep learning has become a popular approach to tackle this task. However, it has been observed that current methods based on deep neural networks tend to favor raw signals as input, disregarding the valuable clinical experience in ECG diagnosis. Approach. In this study, we proposed a novel feature extraction method that generates a pseudo QRS complex signal and a pseudo T, P wave signal for each raw ECG signal using a temporal mask built upon R peak detection. Then a novel dilated residual neural network was trained on the decomposed signal. Main results. We evaluated the performance of our method on the dataset of PhysioNet/CinC 2017 Challenge, achieving an average score of 0.843. The method was further tested on MIT-BIH Atrial Fibrillation Database, and an average score of 0.984 was obtained. Significance. Our proposed ECG signal decomposition technique introduces simple and reliable domain knowledge into deep neural networks, and the dilated residual network provides large and flexible receptive fields, thereby enhancing the performance in the detection of AF. Our method can be extended to many other tasks involving ECG signals.

Block-MUSIC in blade tip timing: Performance study of block snapshot matrix

Periodic nonuniform sampling can be viewed as a combination of uniform sampling and non-uniform sampling. Blade tip timing (BTT) technique conforms to this sampling pattern, where non-uniformity and uniformity are caused by non-uniform probe layout and rotational motion, respectively. By introducing block structure into multiple signal classification (MUSIC) for the extension of snapshot matrix, Block-MUSIC has been proved to be anti-aliasing and can filter out synchronous frequency in BTT application. However, the theoretical justification of the block structure is lacking. In this paper, a performance study of Block-MUSIC is conducted from three aspects: identifiability, stability, and resolution. Additionally, a pseudo 2-dimensional signal model is proposed to represent the periodic nonuniform sampled signal under the assumption of equally spaced probes. To support the performance study, simulations and experiments are carried out to demonstrate how the size of the snapshot matrix, the noise level, and the amplitude of sinusoidal signals affect algorithm performance. Simulation codes are available at https://github.com/superjdg/block-music4btt.

Unsupervised Facial Action Representation Learning by Temporal Prediction.

Due to the cumbersome and expensive data collection process, facial action unit (AU) datasets are generally much smaller in scale than those in other computer vision fields, resulting in overfitting AU detection models trained on insufficient AU images. Despite the recent progress in AU detection, deployment of these models has been impeded due to their limited generalization to unseen subjects and facial poses. In this paper, we propose to learn the discriminative facial AU representation in a self-supervised manner. Considering that facial AUs show temporal consistency and evolution in consecutive facial frames, we develop a self-supervised pseudo signal based on temporally predictive coding (TPC) to capture the temporal characteristics. To further learn the per-frame discriminativeness between the sibling facial frames, we incorporate the frame-wisely temporal contrastive learning into the self-supervised paradigm naturally. The proposed TPC can be trained without AU annotations, which facilitates us using a large number of unlabeled facial videos to learn the AU representations that are robust to undesired nuisances such as facial identities, poses. Contrary to previous AU detection works, our method does not require manually selecting key facial regions or explicitly modeling the AU relations manually. Experimental results show that TPC improves the AU detection precision on several popular AU benchmark datasets compared with other self-supervised AU detection methods.

ADAPTIVE TECHNIQUE TO PHASE ESTIMATION IN PSEUDO-NOISE INFORMATIVE SIGNAL OF DOPPLER RADAR FOR RESCUERS

ADAPTIVE TECHNIQUE TO PHASE ESTIMATION IN PSEUDO-NOISE INFORMATIVE SIGNAL OF DOPPLER RADAR FOR RESCUERS

Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection.

Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal.

Parametric Doppler correction analysis for wayside acoustic bearing fault diagnosis

Wayside acoustic system plays a crucial role in monitoring and diagnosing the status of train wheel bearings. However, due to the signal distortion caused by Doppler effect, the diagnosis accuracy will be seriously disturbed. In this manner, this paper proposes a model-driven Doppler distortion self-tuning method in theory, named as parametric Doppler correction analysis (PDCA). Different from traditional methods, such as instantaneous frequency tracking and Doppler distortion sparse representation, the proposed method aims to abstractly construct a physical model of acoustic signal distortion propagation based on Morse acoustic theory, where the acoustic forward propagation model and reverse reconstruction model are simultaneously built. The scheme consists of four basic steps. Firstly, the physical model with amplitude modulation operator and frequency shift operator is described for the distortion process. Secondly, the pseudo transition signal with the characteristics of energy accumulation and no distortion is obtained via a construction of frequency rearrangement operator and amplitude demodulation operator. Then, pseudo Doppler correction (PDC) is presented to solve the frequency distortion of the received signal, where a high energy accumulation for frequency distribution is designed as optimization function. Especially, quasi Newton algorithm L-BFGS is used to realize the adaptive learning for distortion parameters. Finally, through time-domain interpolation resampling (TIR) technique, the corrected signal can be analytically reconstructed with the optimal parameters. Applying the PDCA scheme to simulated signals and experimental signals, its validity is verified. The comparison with the instantaneous frequency ridge extraction approach further indicates the proposed model-driven method can reach a more accurate correction result in a faster and adaptive process.

Quantitative Evaluation of Pseudo Strain Signals Caused by Yarn Structural Deformation

Quantitative Evaluation of Pseudo Strain Signals Caused by Yarn Structural Deformation

Highly sensitive monolithic optoelectronic receiver with large-area avalanche photodiode for optical wireless communication at low data rates

A highly sensitive monolithic optical receiver, applicable in optical fiber and wireless communication is presented. The proposed chip is capable of processing data rates of up to 10 Mb / s and offers compatibility to standard CMOS logic by creating full 3.3-V output swing. The photodetector of the system is carried out as a large-area fully integrated avalanche photodiode with a diameter of 800 μm and a high responsivity to red light with a wavelength of 675 nm. The receiver features pseudo differential signal processing with offset cancellation feedback to achieve desensitization to background light irradiation usually appearing with optical wireless communication. Additionally, common-mode feedback was implemented to prevent pulse width distorted output signals. Measurements at 2.5, 5, and 10 Mb / s reveal high sensitivities of −50.4, −47.65, and −42.7 dBm, respectively, for BER < 10 − 9 in dark conditions. For the design, a 0.35-μm CMOS process was chosen.

A Novel Approach for the Characterization of Triangular Modulated Frequency Modulated Continuous Wave Low Probability of Intercept Radar Signals via Application of the Wigner-Ville Distribution

Digital intercept receivers are changing from Fourier-based analysis to classical time-frequency analysis techniques for analyzing low probability of intercept radar signals. This paper presents a novel approach of characterizing low probability of intercept triangular modulated frequency modulated continuous wave radar signals through utilization and direct comparison of the signal processing techniques Wigner-Ville Distribution versus the Reassigned Smooth Pseudo Wigner-Ville Distribution. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Reassigned Smooth Pseudo Wigner-Ville Distribution signal processing technique produced more accurate characterization metrics than the Wigner-Ville Distribution signal processing technique.

An Enhanced Impulsive Noise Suppression Method Based on Wavelet Denoising and ICA for Power Line Communication

Aiming at the problem of noise suppression in power lines, traditional noise suppression methods need to know prior knowledge and other defects. In this paper, blind source separation methods that do not need prior knowledge are selected. In the case of low signal-to-noise ratio, the basic independent component analysis algorithm has poor denoising effect. Therefore, this paper proposes a joint independent component analysis algorithm based on Wavelet denoising and Power independent component analysis (WD-PowerICA). In this work, firstly, the pseudo observation signal is constructed by weighted processing, and the blind separation model of single channel is transformed into a multi-channel determined model. Then, the proposed WD-PowerICA algorithm is used to separate noise and source signals. Finally, the simulation results demonstrate that the proposed algorithm in this paper can effectively separate noise and source signal under low SNR. At the same time, the stronger the α pulse noise is, the closer the WD-PowerICA separated signal is to the source signal. The proposed algorithm is better than the state of the art PowerICA algorithm.

BLDC Motor Drives: A Single Hall Sensor Method and a 160° Commutation Strategy

The application of Hall-based BLDC motors ranges from automotive accessory components such as oil and water pumps to high speed compressors. This paper proposes a new twelve-step unsymmetrical 160° commutation strategy using a single Hall-effect sensor method for brushless DC (BLDC) motor drives. The signal from the Hall-effect sensor is used to generate a number of pseudo Hall signals via a proposed speed-position estimator. The pseudo signals are then employed to control switching logic of a voltage source inverter (VSI). The effectiveness of the single Hall sensor method is examined on the unconventional 160° strategy, and on conventional 120°, 150° and 180° strategies with promising results. The paper presents a detailed comparison of the twelve-step unsymmetrical 160° commutation strategy with six-step voltage space vector that is used in conventional 120° and 180° strategies, and twelve-step symmetrical voltage space vector that is used in 150° strategy. Simulation results are validated via experimental measurements on a laboratory set-up using a real time dSPACE control environment, a representative BLDC motor prototype, a VSI and associated control components.

Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.

Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.

Assessing site signal preservation in reference chronologies for dendro-provenancing.

Regional differences in tree growth can be used to approximate the geographical provenance of ring-width series (‘dendro-provenancing’). This method relies on cross-dated ring-width series (reference chronologies) that are thought to represent the radial growth signal of trees in a given region. Reference chronologies are often established from ring-width series of living tree populations. Frequently, they are too short to allow for investigating the provenance of historical wood. Thus, references are extended by ring-width series from buildings and art-historical objects that exhibit best matching growth patterns with the living tree references. Yet, series from other provenances may erroneously be included. Thereby the local or regional growth signal of the references is progressively contaminated, but this has received little attention to date. I investigate this contamination risk using a simulation approach that allows for generating pseudo site chronologies that preserve the relevant statistical properties of the real site chronologies. While the exact provenance of historical wood is unknown, for simulated ring-width series the provenance is unambiguous. Hence, pseudo reference chronologies may be established while monitoring the signal mixture. Specifically, 15 site chronologies of Norway spruce (Picea abies (L.) H. Karst.) from northeastern Switzerland were used to generate 15 pseudo site growth signals that span 1000 years. The simulation demonstrates that quasi uncontaminated references can be established in ideal circumstances for the study area. However, the thresholds for the similarity in between-series correlation must be very high. Even then, contaminated pseudo references occurred in rare cases during the simulation. Yet, elevation-specific pseudo references were established with lower thresholds. Simulation currently offers the only approach for assessing the contamination risk of reference chronologies, and it allows for elucidating the conditions under which acceptable levels of contamination can be guaranteed. Therefore, the present approach paves the way towards a practical simulation tool for dendro-provenancing.

Combined Power Ratio Calculation, Hadamard Transform and Least Mean Squares Algorithm for Channel Mismatch Calibration in Time-Interleaved ADCs

This paper presents a method for all-digital background calibration of multiple channel mismatches including offset, gain and timing mismatches in time-interleaved analog-to-digital converters (TIADCs). The average technique is used to remove offset mismatch at each channel. The gain mismatch is calibrated by calculating the power ratio of the sub-ADC over the reference ADC. The timing skew mismatch is calibrated by using Hadamard transform for error correction and LMS for timing mismatch estimation. The performance improvement of TIADCs employing these techniques is demonstrated through numerical simulations. Besides, achievement results on the field-programmable gate array (FPGA) hardware have demonstrated the effectiveness of the proposed techniques.&#x0D; KeywordsTime-interleaved analog-to-digital converter (TIADC), channel mismatches, all-digital background calibration&#x0D; References[1] Melamed and S. 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Aromatase inhibitors associated osteonecrosis of jaw: signal refining to identify pseudo safety signals

Background Signal generation through data mining algorithms is an innovative and emerging field in pharmacovigilance. Early detection of safety signals is important for public health safety. However, the possibility of generatingpseudo signals should not be overlooked. Objective Our study aimed to identify potential signals of aromatase inhibitors associated Osteonecrosis of Jaw and assess the possibilities of the safety signal to be a pseudo signal/false positive in FDA Adverse Event Reporting System (FAERS). Setting Spontaneously reported data in FAERS database. Methods Data for this study were obtained from the public release of data in FAERS. OpenVigil, a pharmacovigilance analytical tool was used to access FAERS data. Reporting Odds Ratio (ROR) was used to assess the relation between the drug and adverse event. A value of ROR-1.96SE > 1, (SE-standard error) was considered positive. Main outcome measure Signal strength. Results FAERS database had a total of 15,178 reports for Osteonecrosis of Jaw. Amongst which 617 reports were associated with aromatase inhibitors. Signal strength ROR (lower bound of the 95% CI) for letrozole, anastrozole and exemestane associated Osteonecrosis of Jaw without any background correction was 8.34, 6.64 and 15.14 respectively. Upon removing the reports of concomitantly administered drugs (bisphosphonates and denosumab), signal strength drastically decreased to 0.03, 0.36 and 0.47 for letrozole, anastrozole and exemestane respectively. The signal strength of bisphosphonates and denosumab associated Osteonecrosis of Jaw was not changed significantly upon removal of aromatase inhibitors. Conclusion Our study concluded that the signal generated for aromatase inhibitors associated Osteonecrosis of Jaw in FAERS database can be false positive. Careful background corrections with identification of those risk factors are imperative to exclude false positive results.

Improved autoregressive model for correction of noise serial correlation in fast fMRI.

PurposeIn rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T‐statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high‐order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction.MethodsFast fMRI images were acquired at rest (null data) using a multiband simultaneous multi‐slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc) that employs the corrected Akaike information criterion voxel‐wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event‐related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth).ResultsThe measured false positive characteristics agreed well with the theoretical curve when using the ARAICc, and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method.ConclusionThe ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.