Detection Filter Research Articles

DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites

BackgroundWe have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.MethodsFor this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.ResultsThe updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.ConclusionsThe updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.

Информационно-аналитическая система детектирования движения объектов на пешеходном переходе

To manage pedestrian and vehicle flows at intersections, systems are implemented that use adaptive traffic light models, adjusting time intervals based on the volume of pedestrians and vehicles. These systems include video cameras that monitor road user movements, enhancing real-time traffic control. This paper introduces an information and analytical system for managing transport and pedestrian flows using the YOLO neural model, which enables object recognition. The system performs several operations: converting the original image to gray scale, applying Gaussian blurring, detecting object boundaries through the Canny filter and fuzzy logic for edge detection, and contour processing, where each identified contour is assigned a unique number. The neural network then compares detected contours to the training sample data, determining whether the object is a person or a vehicle. The paper presents experimental results for these algorithms in object recognition. Modified software and images of intersections with pedestrian crossings captured from street video cameras in Kursk were used in the experiments. The recognition accuracy rate from the experiments was 72.4%.

High quality and sensitivity phononic crystal channel drop filter to detect ethyl lactate in mixtures of ethyl lactate and 2-butoxy ethanol

This paper introduces a new sensing approach utilizing a solid-solid phononic crystal (PnC) channel drop filter structure for the detection of varying molar fractions of Ethyl lactate within a mixture of Ethyl lactate and 2-butoxy ethanol. The sensor features a two-dimensional PnC constructed from poly methyl methacrylate as the background material, incorporating a regular arrangement of circular Tungsten columns. The design integrates a bus waveguide linked to two interconnected ring resonators, which are coupled to a drop waveguide. Each ring resonator is equipped with three strategically positioned pillars near the coupling region, allowing for the accommodation of varying concentrations of Ethyl lactate. The sensor’s performance is significantly influenced by the ring resonators and the integrated pillars. It identifies specific resonance frequencies that shift in response to changes in Ethyl lactate concentrations within the resonators. The transmission resonance frequency exhibits valuable sensitivity to these concentration variations, reflecting the unique sound velocities and mass densities associated with each level of Ethyl lactate. The effectiveness of the proposed sensor is validated through coupled mode theory, demonstrating a close match with the device’s observed behavior. The measured frequency range spans from 1.972 MHz to 1.979 MHz with a step size of 1 Hz. Notably, the sensor displays considerable characteristics, including an average quality factor of 90,613, an average sensitivity of 3816 Hz, an average figure of merit of 172, an average signal-to-noise ratio of 137, an average resolution of 22 Hz, an average damping ratio of 0.56 × 10− 5, and an average limit of detection of 34 × 10− 5. These results underscore the potential of the channel drop filter for the accurate detection of Ethyl lactate concentrations with high quality factor and sensitivity. The sensor can effectively identify variations in Ethyl lactate levels, which are prevalent in various applications, including pharmaceuticals and food additives.

APFC: Adaptive Particle Filter for Change Point Detection of Profile Data in Manufacturing Systems

APFC: Adaptive Particle Filter for Change Point Detection of Profile Data in Manufacturing Systems

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.

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme.

Fractional differentiation based image enhancement for automatic detection of malignant melanoma

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Collaborative fault detection and diagnosis method for dual single-axis RINSs configuration in GNSS-denied environment

To ensure the reliability and safety of the long-endurance navigation, two or more rotational inertial navigation systems (RINSs) are usually equipped on ships. Through the redundant configuration, fault detection and diagnosis (FDD) can be realized. In this paper, a collaborative FDD method applied to dual homogeneous single-axis RINSs is proposed. The method utilizes geometric constraint observation to construct a detection filter, processes the filtering outputs through the modified Bayesian algorithm, and incorporates a fault confirmation stage to achieve fault diagnosis with stronger robustness. Besides, an azimuth gyro drift variation estimation method using least-squares is further proposed by exploiting the difference in Schuler oscillation mode resulting from an azimuth gyro fault as opposed to the fault-free state when the system is damped by the log. The efficiency and reliability are demonstrated through comparative and repeated experiments. According to the results based on repeated experiments, the proposed method achieves a comprehensive correct diagnosis rate of over 99% across various fault scenarios. When relatively significant faults occur, the average diagnosis time for horizontal gyros, horizontal accelerometers, and azimuth gyros is 0.4569h, 0.2686h, and 1.2204h respectively. In the case of more subtle faults, although the average diagnosis time is extended to 0.5920h, 0.2991h, and 1.9641h, the effectiveness of the method is still well demonstrated.

Subsurface interconnection beneath the Mio-Plio-Quaternary volcanoes of the Ain Leuh causse (Middle Atlas, Morocco): Structural framework and emplacement mechanisms

In the Ain Leuh causse, located southwest of the Middle Atlas Volcanic Province (MAVP), a combined study using aeromagnetic data, fieldwork, and remote sensing is carried out to improve our understanding of the subsurface structure and its correlation with surface structural and volcanic features. The tectonically controlled emplacement mechanisms of the MAVP, which continue to hold undisclosed aspects, were investigated as well. Numerous techniques and mathematical filters for edge detection and 2D modelling were used. The main observations show that the positive magnetic anomalies in the study area are mainly related to the magmatic activity, which was organized into two main episodes: i) Triassic, with a tholeiitic affinity, well recorded along the NE-SW inherited Variscan faults; and ii) Mio-Plio-Quaternary, with an alkaline affinity, forming scoria cones, maars, and lava flows. They are also expressed as hidden domes, laccoliths, and interconnected magma bodies revealed by 2D modelling of the aeromagnetic data. Uncommon coarse-grained undersaturated alkaline rocks crop out in Talzast dome (Talzastite, Fasinites, and Ankaratrite). They are unique in the MAVP and restricted to this part of the Middle Atlas. 2D aeromagnetic modelling has revealed the presence of shallow magmatic rocks bodies beneath Talzast dome. The extracted structural lineaments using aeromagnetic data and remote sensing reveal that the structural framework of the study area is controlled by NE-trending faults reaching depths of up to 3000 m, which is the depth of the magnetic signature of the faults. Additionally, NNE, NNW and N-trending short-length lineaments are defined and rooted at depths up to 3000 m. The predominant NE-SW structural trend may be linked to the reactivation of Hercynian faults during Alpine tectonics. These faults likely controlled the emplacement of the Mio-Plio-Quaternary volcanism in the area. The magma reservoirs that would have fed the various volcanic edifices are located at a shallow depth of less than 500–2000 m.

Airside Surveillance by Computer Vision in Low-Visibility and Low-Fidelity Environment

Low visibility at airports can significantly impact airside capacity, leading to ground delays and runway/taxiway incursions. Digital tower technology, enabled by live camera feeds, leverages computer vision to enhance airside surveillance and operational efficiency. However, technical challenges in digital camera systems can introduce low-fidelity transmission effects such as blurring, pixelation, or JPEG compression. Additionally, adverse weather conditions like rain and fog can further reduce visibility for tower controllers, whether from digital video or out-of-tower views. This paper proposes a computer vision framework and deep learning algorithms to detect and track aircraft in low-visibility (due to bad weather) and low-fidelity (due to technical issues) environments to enhance visibility using digital video input. The framework employs a convolutional neural network for aircraft detection and Kalman filters for tracking, especially in low-visibility conditions. Performance enhancements come from pre- and postprocessing algorithms like object filtering, corrupted image detection, and image enhancement. It proves effective on an airport video dataset from Houston Airport, enhancing visibility under adverse weather conditions.

Exploring Hybrid Techniques for Enhanced Pitch Estimation in Speech Processing

aims: 1. To develop a hybrid approach combining the Pitch Estimation Filter (PEF) and Cepstrum Pitch Determination (CPD) methods for pitch detection in audio signals. 2. To conduct comparative analysis with existing pitch detection methodologies, including Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics (SRH) and Cepstrum Pitch Determination (CEP), to assess the performance and accuracy of the proposed hybrid approach. 3. To evaluate the effectiveness of the hybrid approach in various real-world applications such as speech recognition and music transcription, using performance metrics including Gross Pitch Error (GPE) and classification accuracy through a K-Nearest Neighbors (KNN) classifier. background: The study discussed the difficulties in assessing pitch detection algorithms in real-world applications, especially when it comes to audio synthesis and music production. Prominent performance metrics and criteria pertinent to pitch tracking in interactive music applications were identified by the authors through comprehensive user studies and surveys with audio engineers and professional musicians. The results demonstrated the need for user-centered design approaches in algorithm development and evaluation by emphasizing the significance of taking user preferences and practical requirements into account when evaluating the effectiveness of pitch detection algorithms. objective: 1. To develop a hybrid approach combining the Pitch Estimation Filter (PEF) and Cepstrum Pitch Determination (CPD) methods for pitch detection in audio signals. 2. To conduct comparative analysis with existing pitch detection methodologies, including Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics (SRH) and Cepstrum Pitch Determination (CEP), to assess the performance and accuracy of the proposed hybrid approach. 3. To evaluate the effectiveness of the hybrid approach in various real-world applications such as speech recognition and music transcription, using performance metrics including Gross Pitch Error (GPE) and classification accuracy through a K-Nearest Neighbors (KNN) classifier. method: Proposed PEF+CEP result: Finally, a comparison and analysis of different pitch detection techniques revealed how well they performed in terms of important evaluation metrics like accuracy, specificity, sensitivity, and gross pitch error (GPE). Conventional methods such as Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics(SRH) and Cepstrum Pitch Determination (CEP) perform admirably in terms of specificity and accuracy, but they are not very effective in terms of sensitivity and GPE. On the other hand, the suggested hybrid approach, Proposed PEF+CEP, offers a noteworthy enhancement in accuracy, attaining a remarkable 98.8%, in addition to a sensitivity of 99.2%. The hybrid approach exhibits a slightly higher GPE than some traditional methods, but these minor deviations are outweighed by the significant improvements in accuracy and sensitivity that it offers. Furthermore, the Proposed PEF+CEP method is a promising solution for reliable and accurate pitch detection in speech processing applications because it strikes a strong balance between computational efficiency, training time, model size, and convergence rate. The suggested method offers notable improvements in pitch detection accuracy and reliability while addressing the drawbacks of separate approaches by utilizing the advantages of both PEF and CEP techniques. As a result, the suggested PEF+CEP approach stands out as a significant advancement in speech processing, offering enhanced functionality and versatility in a range of real-world settings. conclusion: Finally, a comparison and analysis of different pitch detection techniques revealed how well they performed in terms of important evaluation metrics like accuracy, specificity, sensitivity, and gross pitch error (GPE). Conventional methods such as Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics(SRH) and Cepstrum Pitch Determination (CEP) perform admirably in terms of specificity and accuracy, but they are not very effective in terms of sensitivity and GPE. On the other hand, the suggested hybrid approach, Proposed PEF+CEP, offers a noteworthy enhancement in accuracy, attaining a remarkable 98.8%, in addition to a sensitivity of 99.2%. The hybrid approach exhibits a slightly higher GPE than some traditional methods, but these minor deviations are outweighed by the significant improvements in accuracy and sensitivity that it offers. Furthermore, the Proposed PEF+CEP method is a promising solution for reliable and accurate pitch detection in speech processing applications because it strikes a strong balance between computational efficiency, training time, model size, and convergence rate. The suggested method offers notable improvements in pitch detection accuracy and reliability while addressing the drawbacks of separate approaches by utilizing the advantages of both PEF and CEP techniques. As a result, the suggested PEF+CEP approach stands out as a significant advancement in speech processing, offering enhanced functionality and versatility in a range of real-world settings. Pitch detection algorithms could become even more complex and effective with more research and development in this area, enabling improvements in text-to-speech synthesis, speaker Identification, And Speech Recognition, Among Other Fields. other: Nil

Advancing potential field data analysis: the Modified Horizontal Gradient Amplitude method (MHGA)

Enhancing the detection accuracy of the edges of the geological features within the subsurface remains a significant objective in geophysical data interpretation. Despite numerous advancements, approaches stemming from the directional gradients of gravity and magnetic fields still grapple with challenges such as low-resolution outcomes and susceptibility to noise contamination. In this study, we introduce a novel filtering framework based on the total horizontal gradient and its derivatives, designed to yield more precise and coherent edges free from false boundaries or disruptive artifacts. Validation using synthetic Bishop complex magnetic and gravity datasets, alongside Tuangiao aeromagnetic data from Vietnam, substantiates the robustness and applicability of our modified approach. Furthermore, recognizing the inherent susceptibility of edge detection filters to noise contamination resulting from directional derivatives, we employ the recently developed modified non-local means (MNLM) algorithm to alleviate noise effects prior to the analysis of noisy synthetic and real datasets. Our findings confirm the efficacy of the proposed method in reducing false artifacts and identifying edges with heightened precision, positioning MHGA as a valuable alternative for processing potential field data.

Development and characterization of log periodic feedline based filter for water leakage detection in size invariant PVC pipes

In this proposed work, water leakage detection in Polyvinyl chloride (PVC) pipes has been investigated using a ring filter connected to log-periodic feedlines (LPF). The proposed filter has a well-structured ring design to adjust the PVC pipes and operates between 0.3 GHz & 0.7 GHz. Using a signal generator & analyzer, attenuation measurements are obtained to analyze the impact of water leakage. PVC pipes with varying diameters (3 inches, 2 inches, 1.5 inches, & 1 inch) are connected with water stoppers to control the water flow during experiments. The leak is contained within a plastic tub to make measurements easier. Results show a good connection between attenuation & water level for all pipe diameters, supporting the LPF-based ring filter’s effectiveness as a leakage detector. An analogous circuit model is created to evaluate the filter’s performance further. At the same time, the resonant parameters, such as Q-factors & resonant frequencies, are retrieved from S21 magnitude measurements in the operating frequency range. The proposed methodology is shown to be accurate & reliable, offering significant potential for water leakage detection in PVC pipe systems. By accurately detecting water leakage, this technology enables prompt repairs, reducing water loss & associated costs, enhancing water infrastructure, and promoting sustainable water management.

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.

Tectonics and seismicity of the Gulf of Aden: Contributions of edge detection filters applied to potential field data

Several studies have applied edge detection filters based on potential field derivatives, ratios and statistical-based tools to evaluate structures and provide interpretations. These edge filters provide different responses that may lead to false interpretations, making the selection of an effective filter that can provide optimum response difficult for users. Evaluating the reliability of the edge filters and avoiding false interpretations of the geologic structures requires simultaneous evaluation of filters applied to the same set of theoretical models. By using 2.5D models of gravity and magnetic synthetic data, we evaluate the performance of 21 edge detection filters based on different scenarios. The outcome of the various tests shows that filters of the third class give best results, with reference to the second and first class, and present good to moderate resolution in gravity and magnetic models. This comparison provides a means of inferring the tectono-structural styles, having generated multiple results from different filters and screening for artefacts (i.e., false impressions). Thereafter, we applied the edge enhancement filters to the magnetic and free-air gravity data of Aden Rift. Boundaries corresponding to seafloor spreading, divergent plate and transform faults were identified. The boundaries correlate well with the source locations of earthquakes at shallow depths. In addition to the dominant extensional tectonic regime, we suggest that the local fault system influenced by the Afar plume provides insights to the complex interaction between magmatic, sedimentary, and tectonic forces, shaping the geology of the Arabian-Nubian triple junction and contributes to the dynamics of the Gulf of Aden. The occurrence of earthquake activities along normal and transform fault zones which correlates with identified structural boundaries resulting from the application of edge detection filters provides a novel application to the study of rifting processes by multiple observation techniques.

Spectrum Sensing Using Cooperative Matched Filter Detector in Cognitive Radio

The vast rise in the number of internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faces many issues, such as complexity in sensing, leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, many spectrum sensing frameworks were introduced in the literature. In this paper, an adaptive threshold matched filter detector and a cooperative matched filter detector frameworks are utilized to detect the spectrum and resolve the issues above. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors results and proficiency, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values compared to previous work.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

StarBright Dyes — Simplifying Flow Cytometry with 8-color No Compensation Panels

Abstract Flow cytometry is a powerful technique, allowing a vast amount of information to be obtained from a single sample. A well-designed panel is vital for good quality data. One aspect of panel design is to use fluorophore combinations that avoid close or overlapping spectra. If not, the panel will risk having high spillover, spreading, and reduced population resolution. We have designed 8-color, no compensation panels for conventional flow cytometry where the fluorophores used have distinct spectra, therefore they have minimal signal into detection filters other than their own. This removes the need for post-acquisition compensation, saving time, as well as ensuring high resolution of cells due to the lack of spread. We show 8-color immunophenotyping panels, acquired on a 5-laser ZE5 Cell Analyzer, for human peripheral blood and mouse splenocytes which require no compensation. B cell, cytotoxic and memory T cell, natural killer cell, and monocyte populations were easily identified. These panels can also be used as a backbone for building larger panels by adding additional markers to detect other cells of interest. Many antibodies incorporated into these panels, have been conjugated to StarBrightTM Dyes which have narrow excitation and emission profiles, making them ideal for inclusion in no compensation panels. StarBright Dyes also simplify panel design as they work with all buffers and most fixatives making them suitable for use in a wide range of protocols.

Highly-selective optical filter for NADH fluorescence detection in multiphoton microscopy

Colorectal cancer (CRC) is a pressing global health concern, emphasizing the need for early detection tools. In this study an optical filter for precise detection of nicotinamide adenine dinucleotide (NADH) fluorescence via two-photon excitation fluorescence (TPEF) was developed. Fabricated with silicon dioxide and titanium dioxide thin films in a Fabry-Perot structure, the filter achieved a peak transmittance of about 95% at 483 nm, with a 12 nm full-width at half maximum. TPEF measurements using a tailored setup and NADH liquid phantoms underscored the filter's significance in selectively capturing NADH fluorescence while mitigating interference from other fluorophores. This work marks a substantial stride towards integrating multiphoton microscopy into conventional colonoscopy, enabling non-invasive, objective optical biopsy for colorectal tissue analysis. Further refinements of the experimental setup are imperative to advance tissue differentiation and enhance CRC diagnosis.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).