Effect of Noise Filtering in an Ultraviolet Detector of UHPLC on the Statistical Reliability of Chemometrically Evaluated Repeatability.

A noise filter, which is usually attached to a detector for chromatography, was applied for the improvement of a signal-to-noise ratio (S/N) on a chromatogram. The objective of this paper is to elucidate the effect of noise filtering in an UV detector of ultra HPLC (UHPLC) on the statistical reliability of chemometrically evaluated repeatability by the function of mutual information (FUMI) theory. To examine the statistical reliability of chemometrically evaluated repeatability in the UHPLC system associated with noise filtering, the standard deviation (SD) values of the area in baseline fluctuations with peak region k (s(k)) were obtained from six chromatograms with noise filtering. Further, the average of s(k) values (σ̂) was calculated from the s(k) values (n = 6) to be alternatively applied as the population SD. All s(k)/σ̂ values were within the 95% confidence intervals (CIs) at the freedom degree of 50, indicating the chemometrically estimated relative SD (RSD) of a peak area and RSD by repeated measurements of at least 50 times had equivalent reliability.

Active disturbance rejection control based on a cascade estimator composed of reduced-order and full-order extended state observers.

This paper presents a pioneering cascade estimator, CRESO, which merges reduced-order and full-order extended state observers (ESO) in a novel manner. CRESO is designed to navigate the trade-off between robustness, estimation accuracy, and noise amplification inherent in active disturbance rejection control (ADRC) schemes. An analysis in the frequency domain substantiates CRESO's performance and robustness capabilities compared to those of single-level ESO and cascade ESO (CESO). These features are quantified using practical metrics, such as stability margins, sensitivity bandwidth, and estimation error at low frequencies. Additionally, the discussion encompasses the impact of selecting bandwidths for the cascade levels of CRESO on noise suppression. Experimental validation on a synchronous buck converter demonstrates the effectiveness of CRESO-based ADRC against control gain uncertainties, frequency-varying external disturbances, and sensor noise. The results highlight the advantages of the proposed approach over ADRC strategies employing singular ESO, two-level CESO, and two independent ESOs, as evidenced by several quality indices derived from the tracking errors and control signals.

Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm.

Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.

An efficient multi-class classification of skin cancer using optimized vision transformer.

Skin cancer is a pervasive and deadly disease, prompting a surge in research efforts towards utilizing computer-based techniques to analyze skin lesion images to identify malignancies. This paper introduces an optimized vision transformer approach for effectively classifying skin tumors. The methodology begins with a pre-processing step aimed at preserving color constancy, eliminating hair artifacts, and reducing image noise. Here, a combination of techniques such as piecewise linear bottom hat filtering, adaptive median filtering, Gaussian filtering, and an enhanced gradient intensity method is used for pre-processing. Afterwards, the segmentation phase is initiated using the self-sparse watershed algorithm on the pre-processed image. Subsequently, the segmented image is passed through a feature extraction stage where the hybrid Walsh-Hadamard Karhunen-Loeve expansion technique is employed. The final step involves the application of an improved vision transformer for skin cancer classification. The entire methodology is implemented using the Python programming language, and the International Skin Imaging Collaboration (ISIC) 2019 database is utilized for experimentation. The experimental results demonstrate remarkable performance with the different performance metrics is accuracy 99.81%, precision 96.65%, sensitivity 98.21%, F-measure 97.42%, specificity 99.88%, recall 98.21%, Jaccard coefficient 98.54%, and Mathew's correlation coefficient (MCC) 98.89%. The proposed methodology outperforms the existing methodology.

A Novel Image Processing Method for Obtaining an Accurate Three-Dimensional Profile of Red Blood Cells in Digital Holographic Microscopy.

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.

Vacuum cleaving of superconducting niobium tips to optimize noise filtering and with adjustable gap size for scanning tunneling microscopy.

Superconducting (SC) tips for scanning tunneling microscopy (STM) can enhance a wide range of surface science studies because they offer exquisite energy resolution, allow the study of Josephson tunneling, or provide spatial contrast based on the local interaction of the SC tip with the sample. The appeal of a SC tip is also practical. An SC gap can be used to characterize and optimize the noise of a low-temperature apparatus. Unlike typical samples, SC tips can be made with less ordered materials, such as from SC polycrystalline wires or by coating a normal metal tip with a superconductor. Those recipes either require additional laboratory infrastructure or are carried out in ambient conditions, leaving an oxidized tip behind. Here, we revisit the vacuum cleaving of an Nb wire to prepare fully gapped tips in an accessible one-step procedure. To show their utility, we measure the SC gap of Nb on Au(111) to determine the base temperature of our microscope and to optimize its RF filtering. The deliberate coating of the Nb tip with Au fully suppresses the SC gap and we show how sputtering with Ar+ ions can be used to gradually recover the gap, promising tunability for tailored SC gaps sizes. • Oxide free superconducting STM tips • RF filter optimization.

Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing.

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators.

In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel's tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.

A new state observer for active disturbance rejection control with measurement noise and output delay using the PDE-backstepping predictor.

This paper designs a new observer to address the issue of disturbance rejection in nonlinear plants with sense delay and measurement noise. The effect of sensor delay is characterized as a transport equation, and a predictor-based extended state observer has been designed using the PDE (partial differential equation) backstepping method. A cascaded ESO (extended state observer) architecture has been introduced to further reduce the limitation of measurement noise on observer bandwidth. This new cascade observer is capable of rapidly and accurately reconstructing system signals and compensating for output delay while avoiding the amplification of measurement noise. The numerical example has demonstrated the efficacy of the proposed observer structure, and extensive comparative experimental results have further confirmed its superiority.

Integrated Filter Design for Analog Field Mill Sensor Interface.

The design process of an integrated bandpass filter targeted for the noise filtering stage of the synchronous demodulation unit of an electric field mill sensor interface is presented. The purpose of this study of filter integration techniques is to avoid the challenging and, in some cases, impossible passive element integration process and to incorporate the final filter design in an entirely integrated field mill sensing system with superior performance and an optimized silicon-to-cost ratio. Four different CMOS filter implementations in the 0.18 µm process of XFAB, using OTA (Operational Transconductance Amplifier)-based configurations for passive element replacement in cascaded filter topologies and leapfrog techniques, are compared in terms of noise performance, total harmonic distortion, dynamic range, and power consumption, as well as in terms of integrability, silicon area, and performance degradation at process corners/mismatches. The optimum filter design performance-wise and process-wise is included in the final design of the integrated analog readout of a field mill sensor, and post-layout simulation results of the total circuit are presented.

Practical fixed-time observer-based generalized cascade predictor design for synchronization of integrator systems with delays and complex unknowns.

This paper studies output feedback leaderless synchronization problem for high-order integrator chains subject to nonlinear dynamics, measurement noises, matched disturbances, and time delays in both input and output. First, we consider the varying normal-scale input delay case and propose a novel extended state observer (ESO)-based predictor device for each agent using relative output of neighbors. Then, in combination with integral sliding mode, leaderless fully distributed synchronization protocols are designed which can highly compensate for delays and unknowns. In particular, the ESO combines the transient performance of fixed-time algorithms with the continuous/smooth, anti-noise and low-cost strengths of linear algorithm via linear structure and time base gains. Also, any disturbances considered in this protocol are not generated by known exosystem and hence the proposed anti-disturbance time delay compensation scheme is more general. Second, aiming to cope with a larger class of input delays, the above device is extended to a ESO-based cascade predictor device. Synchronization analysis is placed in the Lyapunov-Krasovskii functional frame and sufficient conditions are deduced to ensure that the synchronization errors converge to a residual set in fixed time. Detailed numerical simulation studies and thorough comparisons are provided to verify the effectiveness and superiority of the designed ESO and devices.

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering.

BACKGROUND: To obtain phase-contrast X-ray images, single-grid imaging systems are effective, but Moire artifacts remain a significant issue. The solution for removing Moire artifacts from an image is grid rotation, which can distinguish between these artifacts and sample information within the Fourier space. However, the mechanical movement of grid rotation is slower than the real-time change in Moire artifacts. Thus, Moire artifacts generated during real-time imaging cannot be removed using grid rotation. To overcome this problem, we propose an effective method to obtain phase-contrast X-ray images using instantaneous frequency and noise filtering. RESULT: The proposed phase-contrast X-ray image using instantaneous frequency and noise filtering effectively suppressed noise with Moire patterns. The proposed method also preserved the clear edge of the inner and outer boundaries and internal anatomical information from the biological sample, outperforming conventional Fourier analysis-based methods, including absorption, scattering, and phase-contrast X-ray images. In particular, when comparing the phase information for the proposed method with the x-axis gradient image from the absorption image, the proposed method correctly distinguished two different types of soft tissue and the detailed information, while the latter method did not. CONCLUSION: This study successfully achieved a significant improvement in image quality for phase-contrast X-ray images using instantaneous frequency and noise filtering. This study can provide a foundation for real-time bio-imaging research using three-dimensional computed tomography.

Improving Actuator Wearing Using Noise Filtering.

Actuator, mostly valve, wearing is an important factor of the overall industrial control system operational cost. Actuator operational wear strongly depends on its operation. Highly utilized elements have a tendency to degrade faster. Therefore, the maintenance teams prefer to minimize their moves. In contrary, control engineers need the actuators to actively operate in their control loops to mitigate disturbances and follow the desired trajectories. Higher control performance is often achieved with an active use of actuators. Control loop quality depends on the controller setup and loop auxiliary functionality. Properly designed filtering not only facilitates controller action, but also impacts actuator operational wear. Industrial control templates are built using the blockware that is embedded in the existing control system. Distributed control system (DCS) and programmable logic controller (PLC) have a limited number of control algorithms. An engineer has to design the control structure and the associated sensor noise filtering using available functionality. This paper evaluates and measures the impact of noise filtering on the loop performance and on the actuator weariness. Relations between noise filtering time constant, loop performance and valve travel deliver recommendations for control engineers.

Data-driven dynamics-based optimal filtering of acoustic noise at arbitrary location in atomic force microscope imaging.

This paper presents a post-filtering approach to eliminate distortions in atomic force microscope (AFM) images caused by acoustic noise from an unknown location. AFM operations are sensitive to external disturbances including acoustic noise, as disturbances to the probe-sample interaction directly results in distortions in the sample images obtained. Although conventional passive noise cancellation has been employed, limitation exists and residual noise still persists. Advanced online control techniques face difficulty in capturing the complex noise dynamic and limited system bandwidth imposed by robustness requirement. In this work, we propose a dynamics-based optimal filtering technique to remove the acoustic-caused distortions in AFM images. A dictionary-approach is integrated with time-delay measurement to localize the noise source and estimate the corresponding acoustic dynamics. Then a noise-to-image coherence minimization approach is proposed to minimize the acoustic-caused image distortion via a gradient-based optimization to seek an optimal modulator to the acoustic dynamics. Finally, the filter is obtained as the finite-impulse response of the optimized acoustic dynamics. Experimental implementation is presented and discussed to illustrate the proposed technique.

A universal, high-performance ECG signal processing engine to reduce clinical burden.

BACKGROUND: Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. OBJECTIVE: This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). METHODS: Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. RESULTS: HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. CONCLUSION: HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Critical comparison of background correction algorithms used in chromatography.

The objective of the present work was to make a quantitative and critical comparison of a number of drift and noise-removal algorithms, which were proven useful by other researchers, but which had never been compared on an equal basis. To make a rigorous and fair comparison, a data generation tool is developed in this work, which utilizes a library of experimental backgrounds, as well as peak shapes obtained from curve fitting on experimental data. Several different distribution functions are used, such as the log-normal, bi-Gaussian, exponentially convoluted Gaussian, exponentially modified Gaussian and modified Pearson VII distributions. The tool was used to create a set of hybrid (part experimental, part simulated) data, in which the background and all peak profiles and areas are known. This large data set (500 chromatograms) was analysed using seven different drift-correction and five different noise-removal algorithms (35 combinations). Root-mean square errors and absolute errors in peak area were determined and it was shown that in most cases the combination of sparsity-assisted signal smoothing and asymmetrically reweighted penalized least-squares resulted in the smallest errors for relatively low-noise signals. However, for noisier signals the combination of sparsity-assisted signal smoothing and a local minimum value approach to background correction resulted in lower absolute errors in peak area. The performance of correction algorithms was studied as a function of the density and coverage of peaks in the chromatogram, shape of the background signal, and noise levels. The developed data-generation tool is published along with this article, so as to allow similar studies with other simulated data sets and possibly other algorithms. The rigorous assessment of correction algorithms in this work may facilitate further automation of data-analysis workflows.

An improved immune plasma algorithm with a regional pandemic restriction.

The coronavirus (COVID-19) and its global effect have increased the interests of researchers from different disciplines to the medical methods such as immune or convalescent plasma treatment. Immune Plasma algorithm (IPA) that is the first meta-heuristic referencing the steps of the immune plasma treatment as the name implies has been proposed recently and its potential has been investigated. In this study, a pandemic management strategy based on limiting the free movements between regions was modeled and integrated into the workflow of the IPA and a new variant called regional IPA (rIPA) was introduced. For analyzing the contribution of the proposed method, twelve numerical benchmark problems were solved. Also, the performance of the rIPA was investigated by solving a new big data optimization problem that requires minimization of the measurement noise of electroencephalography signals. The results obtained by the rIPA were compared with the fourteen well-known and state-of-art meta-heuristics. Comparative studies showed that managing the relationship between the individuals of the population as in the proposed regional model significantly contributes to the capabilities and rIPA outperforms other meta-heuristics for most of the test cases.

Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing.

BACKGROUND: Systems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation. RESULTS AND DISCUSSION: We developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility. CONCLUSION: We introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

Cascade extended state observer for active disturbance rejection control applications under measurement noise.

The extended state observer (ESO) plays an important role in the design of feedback control for nonlinear systems. However, its high-gain nature creates a challenge in engineering practice in cases where the output measurement is corrupted by non-negligible, high-frequency noise. The presence of such noise puts a constraint on how high the observer gains can be, which forces a trade-off between fast convergence of state estimates and quality of control task realization. In this work, a new observer design is proposed to improve the estimation performance in the presence of noise. In particular, a unique cascade combination of ESOs is developed, which is capable of fast and accurate signals reconstruction, while avoiding over-amplification of the measurement noise. The effectiveness of the introduced observer structure is verified here while working as a part of an active disturbance rejection control (ADRC) scheme. The conducted numerical validation and theoretical analysis of the new observer structure show improvement over standard solution in terms of noise attenuation.