Detection Filter Research Articles

Practical study for comparing edge detection filters in digital image processing

Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.

SOCF: A correlation filter for real-time UAV tracking based on spatial disturbance suppression and object saliency-aware

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to its high tracking accuracy and computing speed. However, when similar object disturbances emerge in the background, the response map will generate sub-peaks, which may eventually lead to tracking failure. Meanwhile, the lack of attention to the tracked object can also cause tracking performance degradation. To these concerns, this paper proposes a novel correlation filter algorithm for real-time aerial tracking based on spatial disturbance suppression and object saliency-aware, i.e., SOCF. Firstly, this paper designs a novel spatial disturbance suppression strategy. Using the temporal information in the historical response maps, we construct a context response map, deviating it from the current response map to detect disturbance information in the background. Then, construct a spatial interference map, divide it into n×n non-overlapping regions, and suppress the negative samples in the disturbance region within the main regression. Furthermore, an object saliency-aware strategy is proposed, using a saliency detection algorithm to calculate the object-aware mask and multiplying it with the detection filter to obtain the object-aware filter. By constructing the object-aware regularization in the training phase, the trained detection filter focuses more on the object itself and can effectively separate the object from the background. Extensive experiments on four widely used unmanned aerial vehicle (UAV) datasets demonstrate that the proposed SOCF tracker achieves high tracking performance. Meanwhile, our tracker can maintain real-time aerial tracking at 48 FPS on a single CPU.

3D DENSITY-GRADIENT BASED EDGE DETECTION ON NEURAL RADIANCE FIELDS (NERFS) FOR GEOMETRIC RECONSTRUCTION

Abstract. Generating geometric 3D reconstructions from Neural Radiance Fields (NeRFs) is of great interest. However, accurate and complete reconstructions based on the density values are challenging. The network output depends on input data, NeRF network configuration and hyperparameter. As a result, the direct usage of density values, e.g. via filtering with global density thresholds, usually requires empirical investigations. Under the assumption that the density increases from non-object to object area, the utilization of density gradients from relative values is evident. As the density represents a position-dependent parameter it can be handled anisotropically, therefore processing of the voxelized 3D density field is justified. In this regard, we address geometric 3D reconstructions based on density gradients, whereas the gradients result from 3D edge detection filters of the first and second derivatives, namely Sobel, Canny and Laplacian of Gaussian. The gradients rely on relative neighboring density values in all directions, thus are independent from absolute magnitudes. Consequently, gradient filters are able to extract edges along a wide density range, almost independent from assumptions and empirical investigations. Our approach demonstrates the capability to achieve geometric 3D reconstructions with high geometric accuracy on object surfaces and remarkable object completeness. Notably, Canny filter effectively eliminates gaps, delivers a uniform point density, and strikes a favorable balance between correctness and completeness across the scenes.

Detection of SARS-CoV-2 in high-efficiency particulate air (HEPA) filters of low-cost air purifiers in community-based organizations

This study aims to investigate the presence of SARS-CoV-2 in public spaces and assess the utility of inexpensive air purifiers equipped with high-efficiency particulate air (HEPA) filters for viral detection. Samples were collected from six community-based organizations in underserved minority neighborhoods in Northwest Miami, Florida, from February to May 2022. Reverse transcription–quantitative polymerase chain reaction (RT-qPCR) was used to detect SARS-CoV-2 in air purifier filters and surface swabs. Among 32 filters tested, three yielded positive results, while no positive surface swabs were found. Notably, positive samples were obtained exclusively from child daycare centers. These findings highlight the potential for airborne transmission of SARS-CoV-2 in indoor air, particularly in child daycare centers. Moreover, the study demonstrates the effectiveness of readily available HEPA filters in detecting the virus. Improving indoor ventilation and implementing air filtration systems are crucial in reducing COVID-19 transmission where people gather. Air filtration systems incorporating HEPA filters offer a valuable approach to virus detection and reducing transmission risks. Future research should explore the applicability of this technology for early identification and mitigation of viral outbreaks.

Adaptive memory event-triggered double asynchronous fault detection and security control for fuzzy semi-Markov jump systems under cyberattacks

This paper studies the issue of event-triggered fault detection and security control for fuzzy semi-Markov jump systems (FSMJSs) under deception attacks. In particular, a novel adaptive memory event-triggered mechanism (AMETM) that avoids Zeno behavior is considered to improve the transmission efficiency. Meanwhile, the designed adaptive memory triggering condition includes current and historical triggered data. Furthermore, for the sake of realizing the objective of detecting system fault and implementing safety control simultaneously, a double asynchronous mixed fault detection and security control (MFDASC) module is proposed. The module integrated by the fault detection filter and the dynamic output feedback controller can deal with the mismatch of the premise variables and the modes between the system and the corresponding module. Then, the sufficient conditions for strict fault detect and security control dissipation of closed-loop systems are obtained by utilizing integral inequalities. Ultimately, the validity of the designed method is demonstrated by a tunnel diode circuit example.

Adaptive event-triggered based fault detection filtering for unmanned surface vehicles under DoS attacks

This paper proposes an adaptive event-triggered fault detection filter to warn of faults happening to unmanned surface vehicles in a network. First, the unmanned surface vehicle is modeled as a Markovian jump system which makes allowances for the influences of waves or other disturbances. Then, a fault detection filter is developed to produce crucial residual signals, despite disturbances and denial-of-service attacks. Inputs of the designed filter originate from a resilient adaptive event-triggered mechanism, of which the threshold can be adjusted for saving valuable network resources and mitigating adverse influences of denial-of-service attacks. By using the residual signals, detection evaluation functions are established and a detection logic algorithm is devised. Under this framework, the co-design of both the fault detection filter and the adaptive event-triggered scheme is given. Finally, the proposed method is proved to be applicable by simulating a real unmanned surface vehicle model.

Pixelated Filter Array for On-Chip Polarized Spectral Detection.

On-chip multi-dimensional detection systems integrating pixelated polarization and spectral filter arrays are the latest trend in optical detection instruments, showing broad application potential for diagnostic medical imaging and remote sensing. However, thin-film or microstructure-based filter arrays typically have a trade-off between the detection dimension, optical efficiency, and spectral resolution. Here, we demonstrate novel on-chip integrated polarization spectral detection filter arrays consisting of metasurfaces and multilayer films. The metasurfaces with two nanopillars in one supercell are designed to modulate the Jones matrix for polarization selection. The angle of diffraction of the metasurfaces and the optical Fabry-Perot (FP) cavities determine the spectrum's center wavelength. The polarization spectral filter arrays are placed on top of the CMOS sensor; each array corresponds to one pixel, resulting in high spectral resolution and optical efficiency in the selected polarization state. To verify the methodology, we designed nine-channel polarized spectral filter arrays in a wavelength range of 1350 nm to 1550 nm for transverse electric (TE) linear polarization. The array has a 10 nm balanced spectral resolution and average peak transmission efficiency of over 75%, which is maintained by utilizing lossless dielectric material. The proposed array can be fabricated using overlay e-beam lithography, and the process is CMOS-compatible. The proposed array enables broader applications of in situ on-chip polarization spectral detection with high efficiency and spectral resolution, as well as in vivo imaging systems.

Titania Nanoparticle-Stimulated Ultralow Frequency Detection and High-Pass Filter Behavior of a Flexible Piezoelectric Nanogenerator: A Self-Sustaining Energy Harvester for Active Motion Tracking.

A significant driving force for the fabrication of IoT-compatible smart health gear integrated with multifunctional sensors is the growing trend in fitness and the overall wellness of the human body. In this work, we present an autonomous motion and activity-sensing device based on the efficacious nucleation of the polar β-phase in an electroactive polymer. Representatively, we investigate the nucleating effect of TiO2 nanoparticles on weight-modulated PVDF-HFP films (PT-5, PT-10, and PT-15) and subsequently prototype a sensing device with the film that demonstrates superior β-phase nucleation. The PT-10 film, with an optimal polar β-phase, shows the highest remnant polarization (2Pr) and energy density of 0.36 μC/cm2 and 22.3 mJ/cm3, respectively, at 60 kV/cm. The films mimic a high pass filter at frequencies above 10 KHz with very low impedance and high ac conductivity values. The frequency-dependent impedance studies reveal an effective interfacial polarization between TiO2 nanoparticles and PVDF-HFP, explicitly observed in the low-frequency region. Consequently, the sensor fabricated with PT-10 as the sensing layer exhibits ultralow frequency detection (25 Hz) resulting from the blood flow muscle oxygenation. The device successfully senses voluntary joint movements of the human body and actively tracks a range of motions, from brisk walking to running. Additionally, through repetitive human finger-tapping motion, the nanogenerator lights up multiple light-emitting diodes in series and charges capacitors of varying magnitudes under 50 s. The real-time human motion sensing and movement tracking modalities of the sensor hold promise in the arena of smart wearables, sports biomechanics, and contact-based medical devices.

Event‐triggered fault detection filter design for networked switched systems with all subsystems unstable and cyber attacks

SummaryThis article focuses on the issue of event‐triggered fault detection filters (FDF) design for networked switched systems. Compared with the existing fault detection literature, this article investigates the situation that all subsystems are unstable, and considers that stochastic cyber attacks will disrupt fault detection. An event‐triggered mechanism (ETM) that excludes Zeno behavior naturally is designed to economize limited network resources. Then considering the ETM‐induced delay term, the dynamics of the initial system combined with the ETM, fault detection filters, and the effects of cyber attacks are formulated as a new augment switched delayed residual model. Based on the constructed multiple piecewise discretized Lyapunov–Krasovskii function (MPDLKF) and the mode‐dependent dwell time (MDDT) method, sufficient conditions are obtained to guarantee globally mean‐square uniformly asymptotically stability of the new augment system. Moreover, a co‐design method of ETM matrices and FDF gains is proposed. Finally, simulation results are provided to verify the feasibility and effectiveness of the proposed method.

Origin of El-Maghara Anticlines, North Sinai Peninsula, Egypt: Insights from Gravity Data Interpretation Using Edge Detection Filters

The gravity data of Gebel El-Maghara area and its surrounding region in northern Sinai Peninsula, Egypt have been subjected to high precision edge detection filters in order to evaluate the structural and tectonic settings of the study area. Various traditional and modern edge detection filters were tested on synthetic gravity data and then the most successful filters in detection the boundaries of the causative bodies have been used to accomplish the study's objectives. By examining the power spectrum of the gravity data, three gravity maps were generated using low-pass, band-pass and high-pass filters, respectively, representing deep, intermediate and shallow gravity anomalies. The STDR filter and its total horizontal derivative were applied to these gravity maps to image the causative structural features (faults and contacts) and construct structural maps at these different depths. The findings demonstrate that the major trend of faults and contacts at deep depths predominantly aligns along the WNW–ESE direction, while at shallow depths, the main orientation is observed along the NE–SW direction. It is worth noting that the trend of structures at shallow depths demonstrates a good agreement with the structural trends derived from both surface structural map and Landsat images. This trend disparity supports the hypothesis that the formation of the anticlines in the study area is linked to thin-skinned tectonic processes associated with the Syrian Arc system.

Thermoluminescence behavior of Ce3+ doped lanthanum tri-borate phosphor for dosimetry applications

The combustion method was used to synthesize Ce3+ doped LaB3O6 phosphors in a range of dopant concentrations. Both the un-doped and Ce3+ doped LaB3O6 samples matched the required reference card, according to X-ray diffraction patterns. TL glow curves of Ce3+ doped LaB3O6 samples at various dopant concentrations were examined by employing various optical detection filter combinations to define TL characteristics. LaB3O6:Ce3+ (1%) and IRSL-TL 565 nm were selected as the suitable sample and appropriate detection filter pairing. The TL maximum with a higher intensity was seen during preheating trials (with 2 °Cs−1) at around 270 °C. The experimental results showed feasible linearity (R2 = 0.998 and b = 1.054) between 5 and 1000 Gy for the dose response of LaB3O6:Ce3+ (1%). The short-term fading test was conducted at various time intervals up to a week. The findings showed that the dosimetric peak strength was almost constant for up to 5 days. However, an additional spurious maximum appeared at the higher temperature zone. Each sample showed an abnormal heating rate effect, which can be explained by localized electrons caught in the intermediate excited state, which is capable of radiative transitions via the conduction band. To find the overlapping peak numbers and determine the kinetic parameters of preheated LaB3O6:Ce3+ (1%), various heating rates, TM-Ts, initial rise, and computerized glow curve deconvolution techniques were utilized. The results suggest that LaB3O6:Ce3+ may be an appropriate material for TL dosimetry applications due to its efficient TL characteristics.

Asynchronously switched fault detection filter design for full-envelope flight vehicle

The fault detection problem of a class of full-envelope flight vehicles with time delays and packet losses is investigated. Their model is established as locally overlapped switched systems to reduce conservatism. The delays and the packet losses are transformed into the parameters of polytopic systems. The switched parameter-dependent fault detection filter (FDF) is then designed. The fault detection system stability and its [Formula: see text] performance under asynchronous switching with average dwell time are analyzed. Moreover, a parity-space–based optimization approach is presented to guarantee that the modified residual signal is sensitive to fault but robust to disturbance. An example of applying this method to the highly maneuverable technology vehicle is given to verify the method’s effectiveness.

A framework for intracranial aneurysm detection and rupture analysis on DSA

BackgroundIntracranial aneurysm is a severe cerebrovascular disease that can result in subarachnoid hemorrhage (SAH), leading to high incidence and mortality rates. Computer-aided detection of aneurysms can assist doctors in enhancing diagnostic accuracy. The analysis of aneurysm imaging holds considerable predictive value for aneurysm rupture. This paper presents a method for the detection of aneurysms and analysis of ruptures using digital subtraction angiography (DSA). MethodsA total of 263 aneurysms were analyzed, with 125 being ruptured and 138 being unruptured. Firstly, a filter based on the eigenvalues of the Hessian matrix was proposed for aneurysm detection. The filter's detection parameters can be automatically obtained through Bayesian optimization. Aneurysms were detected based on their structure and the response of the filter. Secondly, considering the variations in blood flow and morphology among aneurysms in DSA, intensity, texture, and blood perfusion features were extracted from the ruptured aneurysms and unruptured aneurysms. Subsequently, a sparse representation (SR) method was utilized to classify unruptured and ruptured aneurysms. ResultsThe experimental results for aneurysm detection showed that the F1-score was 94.1%. In the classification of ruptured and unruptured aneurysms, the accuracy, sensitivity, specificity, and area under curve (AUC) were 96.1%, 94.4%, 97.5%, and 0.982, respectively. ConclusionThis paper presents a scheme combining an aneurysm detection filter and machine learning, offering a reliable solution for the diagnosis and prediction of aneurysm rupture.

Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection.

The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson's correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.

Enhancing Distributed Radar Detection Performance With Reliable Communications

Despite their potential to significantly enhance target detection capability, distributed radar networks, with or without a central coordinator, have not been sufficiently examined. In this paper, we propose a generalized likelihood ratio test (GLRT)-based constant false alarm rate (CFAR) detector for distributed detection in a radar network, wherein only neighboring nodes require sharing their GLRT values. We obtain the analytical expression for the local GLRT and employ distributed average consensus-based estimation to reach the global metric in the case of noise-free communications links. The exact analytical expressions for the local probability of false alarm (PFA) and probability of detection (PD) are derived and used to obtain the exact analytical expression for the global PFA as well as an approximate expression for the global PD. In the case of noisy communications links, we demonstrate the effects of quantization and different modulation techniques, such as chirp and phase shift keying, on the global detection probability. Our results show that the proposed detector performs significantly better than the non-CFAR energy detector (ED) and provides performance comparable to that of the non-CFAR matched filter (MF) detector. It is shown that the performance of the chirp and binary phase shift keying (BPSK) modulations is similar but better than that of quadrature phase shift keying (QPSK). Our results also show that the uniform quantization of GLRT values with only 4 bits is sufficient to enable each node to achieve performance similar to the global performance achieved in the case of error-free communications links.

Co-Design of Adaptive Event Generator and Asynchronous Fault Detection Filter for Markov Jump Systems via Genetic Algorithm.

This article investigates the co-design problem of adaptive event-triggered schemes (AETSs) and asynchronous fault detection filter (AFDF) for nonhomogeneous higher-level Markov jump systems, involving the hidden Markov model (HMM), higher-level Markov chain (MC), and conic-type nonlinearities. The transformation of the system transition probability can be reflected by the designed higher-level MC. An HMM with another conditional transition probability is applied to detect higher-level Markov processes and make the system be more practical. In order to balance the utilization of network resources and system performance, a novel AETS is proposed and used in the construction of the AFDF. By the Lyapunov theory, sufficient conditions are given to ensure the existences of the AETS and AFDF. It is not only an appropriate tradeoff between the utilization of network resources and system performance, but also reduces the conservatism. Finally, a numerical example is given to detect the faults effectively by the co-designed AFDF.

Networked fault detection for fuzzy systems using a quantizer with event-triggered strategy

This paper concerns with the problem of fault detection for networked fuzzy systems. Considering the digital communication channel, quantization is adopted to make the data transmission feasible and the induced data distortion is converted into a bounded uncertain term. For the purpose of saving network resources, a new quantizer with an event-triggered strategy is designed to reduce the length and the number of the transmitted data packets, simultaneously. In the process of stability analysis, the T-S fuzzy model approach is employed to approximate the nonlinear networked system, and a more flexible fuzzy design method is presented to get the fault detection filter which can detect the sensor fault in the presence of external disturbance. And sufficient conditions are constructed to guarantee the stability of the filter error system in terms of linear matrix inequalities. Finally, the simulation example is given to verify the feasibility of the method.

A novel, low-cost microfluidic device with an integrated filter for rapid, ultrasensitive, and high-throughput bioburden detection

Rapid and accurate bioburden detection has become increasingly necessary for food, health, pharmaceutical and environmental applications. To detect bioburden accurately, and in a highly sensitive manner, we have fabricated a novel microfluidic device with an integrated filter to trap the cells. Bioburden is detected on the filter paper in situ using the redox reaction of fluorescent label resorufin and a portable multichannel fluorometer is used for fluorescence measurement. The microfluidic device was fabricated in a facile, low-cost, and rapid way with microwave-induced thermally assisted bonding. To characterize the bonding quality of the microfluidic cassettes, different tests were performed, and the filter paper material and size were optimized. Primary Bacillus subtilis culture bacterial samples were filtered through the device to validate and investigate the performance parameters. Our results show that a limit of detection (LOD) of 0.037 CFU/mL can be achieved through this microfluidic device whereas the LOD in a normal microfluidic cassette in the fluorometer and the golden standard spectrophotometer are 0.378 and 0.128 CFU/mL respectively. The results depict that three to ten times LOD improvement is possible through this microfluidic cassette and more sensitive detection is possible depending on the volume filtered within a rapid 3 min. This novel microfluidic device along with the fluorometer can be used as a rapid portable tool for highly sensitive, accurate and high-throughput bacterial detection for different applications.

Research on Stellar Occultation Detection with Bandpass Filtering for Oxygen Density Retrieval

Stellar occultation instruments detect the transmission of stellar spectra through the planetary atmosphere to retrieve densities of various atmospheric components. This paper introduces an idea of using instruments with bandpass filters for stellar occultation detection. According to the characteristics of the occultation technique for oxygen density measurement, a full-link forward model is established, and the average transmission under a typical nocturnal atmosphere is calculated with the help of the HITRAN database, occultation simulation and a 3D ray-tracing program. The central wavelength and bandwidth suitable for 760 nm oxygen A-band absorption measurement are discussed. This paper also compares the results of the forward model with GOMOS spectrometer data under this band, calculates the observation signal-to-noise ratio corresponding to different instrument parameters, and target star magnitudes. The results of this paper provide a theoretical basis for the development of a stellar occultation technique with a bandpass filter and guidance on the instrument design.

Methods to improve burst-mode laser spectral purity for high-speed gas-phase filtered Rayleigh scattering.

In the filtered Rayleigh scattering (FRS) technique, Doppler or homogeneously broadened light from weak molecular scattering is separated from orders-of-magnitude stronger elastic scattering from surfaces, windows, particles, and/or droplets using a narrowband filter. In this work, high-speed detection of such weak molecular scattering is enabled by a burst-mode laser system that can achieve a spectral purity of ∼0.999999. This allows for an additional two orders of magnitude of attenuation from a narrowband iodine molecular filter for high-speed detection of gas-phase FRS in the presence of direct surface scattering at 532 nm. The methodology, system characterization, and feasibility of single-shot gas-phase FRS at 100 kHz or higher are presented and discussed.