Detection Filter Research Articles

OSL at elevated temperature of smart chip cards for retrospective dosimetry

Smart chip cards are a promising material in retrospective dosimetry due to their high collectability with low replacement cost. In this study, dose reconstruction using OSL at elevated temperatures for various chip cards was investigated. The approximate spectral emission of the chip cards was determined through a comparison of radiation-induced and intrinsic background TL signals using different detection filters. In the OSL decay curve, a fast component (0.0–0.4 s) was integrated to avoid high intrinsic background signals. To minimize sensitivity changes due to heat treatment, a protocol to measure OSL at 100 °C was developed and the data were compared with those from a similar protocol for OSL measured at room temperature. In the 0.1 - 5 Gy range, linear and power functions were fitted for room temperature and 100 °C OSL protocols, respectively. The minimum detectable dose was evaluated immediately after exposure, with values of 25 mGy for room temperature and 240 mGy for 100 °C OSL being determined. Signal fading was measured up to 30 days after irradiation for the investigated protocols. A dose recovery test was made 10 days after irradiation using various chip cards, and the 100 °C OSL protocol produced more reliable results than the room temperature OSL protocol. As a result, the study emphasizes the necessity of further investigation of OSL at high temperatures using chip cards.

Nano carbon-modified air purification filters for removal and detection of particulate matters from ambient air

Particulate matters (PMs) pose significant risks to human health and the environment, necessitating research to enhance air purification filters and reduce harmful emissions. This study focuses on the preparation of carbon nanomaterials, including graphitic carbon nitride nanosheets (g-C3N4 NSs), reduced graphene oxide (r-GO), and carbon nanotubes (CNT), for modifying filters in air particle monitoring devices. The objective is to investigate the impact of these nanomaterials on enhancing PM adsorption efficiency. Quantitative and qualitative analyses of the modified filters’ adsorption efficiency towards PMs are performed using spectroscopic techniques such as Energy-Dispersive X-ray Spectroscopy (EDX), Inductively Coupled Plasma (ICP), and Laser-Induced Breakdown Spectroscopy (LIBS). The results reveal that CNT-modified filters exhibit superior adsorption efficiency compared to the control, g-C3N4, and r-GO-modified filters. The exceptional performance of CNTs is attributed to their large specific surface area and pore volume. Additionally, LIBS demonstrates its capability to detect heavy metals like Cd, which remain undetected by EDX and ICP. The technique proves sensitive for heavy metal monitoring. This novel approach is expected to garner significant attention and contribute to the development of improved air purification technologies.

Thermoluminescence kinetics in beta-irradiated novel ZnGa2O4:Eu3+ phosphor produced via gel combustion synthesis

Eu3+ doped ZnGa2O4 phosphors with varying dopant concentrations were successfully synthesized using the combustion technique. X-ray diffraction analysis confirmed the consistency of both undoped and Eu3+ doped ZnGa2O4 samples with PDF#38–1240 card structure. The investigation of thermoluminescence (TL) characteristics involved studying TL glow curves in Eu3+ doped ZnGa2O4 samples with different dopant concentrations, employing various optical detection filter combinations. Among these, ZnGa2O4: 2%Eu3+ and the IRSL-TL wide blue band (wbb) proved to be the optimal sample and ideal detection filter pairing for identifying TL features. Following preheating experiments, the TL maximum was observed at around 190 °C, with a heating rate of 2 °Cs−1. The dose-response experiment for ZnGa2O4:2%Eu3+ exhibited satisfactory linearity, with a coefficient of b = 0.988 and an R2 value of 0.989 for doses up to 100 Gy. Moreover, for the dose range of 200–500 Gy, a sublinear relationship was maintained, with a coefficient of b = 0.31 and an R2 value of 0.988. Hoogenstraaten's method, along with the initial rise method and the Tm-Tstop experiment, was also employed in conjunction with a glow curve fitting package. This comprehensive approach facilitated the determination of the number of peaks, trap structure, and kinetic parameters within the thermoluminescence glow curve. Glow curve deconvolution using specialized software revealed the presence of five well-isolated and overlapping TL glow peaks, achieving a figure of merit (FOM) value of 1.39.

Image preprocessing techniques applied on NIR images for fruit bruise detection

This study investigates the transformative potential of image preprocessing techniques when applied to near-infrared (NIR) images for early bruise detection. It emphasizes the nuanced selection of filters to retain essential image features while accentuating bruise characteristics. Filters as noise-reduction tools, rendering bruises more visible without erasing critical details. Subsequently, the limitations of conventional edge detection filters were examined such as Sobel, Prewitt, and Canny, which excel in outlining fruit edges but fall short in delineating bruises. Adaptive thresholding methods were introduced, exemplified by Otsu’s, showcasing their capacity to distinguish objects from backgrounds while acknowledging their challenge in preserving crucial edge pixels. Image enhancement techniques, such as Histogram Equalization, Contrast Stretching, and Sigmoid Correction, enhance fruit edge visibility and elevate bruise detection. In the frequency domain, filters such as Ideal Lowpass, Bandpass, and Highpass were harnessed to accentuate diverse bruise types. The Butterworth filter was introduced, capable of concurrently highlighting all relevant features, a pivotal innovation in comprehensive bruise detection. Through extensive experimentation and analysis of NIR images of various fruit varieties, including plums, peaches, and apples, our findings underscore the significance of tailored preprocessing techniques for optimal fruit bruise detection. These insights offer promise for agricultural industries and quality control processes seeking to enhance fruit quality assessment.

Development of a Medical Image Segmentation Algorithm based on Fuzzy C-Means Clustering

Breast mass segmentation in mammography plays a very important role in computer-aided diagnosis (CAD) systems. In this article, we propose a mammography image segmentation method based on a combined approach. The fuzzy clustering method and thresholding segmentation. Subsequently, we use the wavelet transform and the Canny filter for edge detection.

Segmentation and quantification of venous structures and perivascular spaces in the thalamus in epilepsy using 7 Tesla MRI

Background and purposeEpilepsy is a complex neurological disorder affecting 50 million people worldwide. Persistent seizures may correlate with neural network, microstructural, and vascular changes within the thalamus. These thalamic changes may result from seizure activity or broader alterations involving neuronal vasculature and neuroinflammatory processes linked to glymphatic drainage. Improved resolution with Ultra-high field (UHF) magnetic resonance imaging (MRI) may be useful in identifying possible thalamic vascular abnormalities not otherwise detectable at lower field strengths. Materials and methodsWe outline a novel method which leverages UHF neuroimaging for detection and quantification of vessels and perivascular spaces (PVS) within the thalamus in 25 epilepsy patients and 16 controls, to uncover possible underlying imaging biomarkers of epilepsy. In our analysis, we optimize a MATLAB-based Frangi-based detection tool called Perivascular Space Semi-Automated Segmentation (PVSSAS) to detect thalamic PVSs, and additionally use a second Frangi-based segmentation tool method to automate detection of vascular structures in the thalamus. The resulting PVS and vessel masks were used to quantify differences in the number of vessels, PVS, overlaps, and number of PVS overlaps per vessel detected between groups, using a Hessian detection filter linked on an 18-connected network. ResultsWe found significantly more thalamic PVS (p = 0.0307) and a significant increase in the number of thalamic vessels (p = 0.038) in patients compared to controls. ConclusionHere we have developed a novel process which leverages UHF MRI to quantify and detect thalamic vessels and PVS that may provide a potential neuroimaging biomarker of epilepsy. Statement of SignificanceWe use 7T, ultra-high field MRI and employed an innovative combination of semi-automated perivascular space segmentation and automated vessel segmentation to visualize and quantify vessels and perivascular spaces (PVS) within the thalamus, a highly cited region of interest in epilepsy. To our knowledge, this is the first study to semi-automatically visualize and segment PVS in the thalamus and automatically detect thalamic vessels. We uncovered detectable differences in thalamic vasculature and PVS. These findings suggests that increases in the number of thalamic PVS and vessels may be a potential neuroimaging biomarker in epilepsy. This tool may be useful in the detection of subtle vascular changes in other regions of the brain related to epilepsy or can be employed in other neurological conditions.

Delineation of geological structures of Arta geothermal prospect in Djibouti based on the gravity data analysis and interpretation

The Arta geothermal prospect is located in the southern part of the Gulf of Tadjoura, 45 km west of Djibouti city. This study uses 398 gravity data stations collected by the Djiboutian Office for Geothermal Energy Development (ODDEG) from February to May 2018. The geothermal activity of the Arta area is revealed by surface thermal manifestations, such as near-boiling fumaroles (98 °C). This study aims to delineate the subsurface geological structures responsible for surface geothermal manifestations and define the depth and geometry of the geothermal reservoir in the study area. A Complete Bouguer anomaly was obtained using a Bouguer density of 2.55 g/cm3, derived as an average density from the F-H, G-H and CVUR methods. A regional and residual anomaly trend separation method was applied to focus on the structures of interest in the study area, and a residual Bouguer anomaly map was obtained. Edge detection filters, including Horizontal Derivatives (HD) and an Improved Normalized Horizontal tilt angle (INH), were used to examine the gravity data for edge identification. The density was then modelled in three-dimensional (3-D), utilizing the VOXI Earth Modeling Tool performed in Geosoft Oasis Montaj. The model was constrained using estimated lower (-0.35 g/cm3) and upper (0.65 g/cm3) bound density values from the expected density values of the local geology. The Bouguer and residual anomaly maps show a high-density body in the central part, surrounded by low-anomaly structures. The high-density body is interpreted as an intrusive body covering the basement of the study area. The anomalies imaged from HD and INH indicate a significant number of high gradient anomalies trending N-S, NE-SW, and NW-SE, corresponding to the trend of the geological structures of the Arta geothermal prospect. These high-gradient anomalies can be interpreted as faults, contact zones, or geological boundaries. The 3-D model imaged a high-density anomaly trending N-S in the central part at 2 and 3 km below sea level with a volume of about 126 km3 and an average block density of 2.66 g/cm3, interpreted as magma intrusion or intrusive volcanic rocks. The 3-D vertical cross-sections revealed that the intrusion is visible at depth and that some intrusions could be seen at the surface and near surface. The findings of this gravity study will contribute to the overall and future geothermal exploration and development of the Arta geothermal prospect.

Control strategy for three-phase four-switch active power filter based on positive sequence extraction in stationary coordinate fundamental reference frame

Traditional three-phase four-switch active power filter often uses the method of phase-locked loop (PLL) and low-pass filter for harmonic detection, which seriously affects the detection accuracy. In order to improve the detection accuracy, a lock-free three-phase four-switch active power filter based on fundamental positive sequence extraction is proposed. The harmonic detection adopts the fundamental positive sequence extraction method, and the rotation coordinate axis transformation adopts the phase-locked loop (PLL) free method. Compared with the traditional three-phase four-switch active power filter, this method does not require a phase-locked loop and a low-pass filter, and does not need complex coordinate transformation[2].The control method is simpler. Finally, simulation results prove the effectiveness of the control method.

Field programmable gate array implementation of edge detection system based on an improved sobel edge detector

<p>Field programmable gate array (FPGA) is an integrated circuit consisting of internal hardware blocks with programmable link connections for users to customize operations for a particular application. Link connections can be easily reprogrammed, allowing the FPGA to adapt to changes to the design or even support a new application throughout the department's uptime. One of the important tasks in image processing is image edge detection image, with computer aided, image recognition is concerned with the recognition and classification of objects in an image, so edge detection is an important tool. In this paper, we design filter for edge detection in image processing using FPGA kit. We analysis and implementation of algorithm for image processing on FPGA, load the code and run the results. Comparative analysis with images processed by MATLAB software.</p>

Improved mapping of flood affected villages in India: a novel three-stage approach using PolSAR polarization signatures and ensembled dilated CNNs

ABSTRACT During floods, updated and accurate information on affected human settlements helps save lives and reduces time to rescue. Therefore, approaches that can provide reliable information during floods via the use of all-weather and real-time functional technology is highly needful. The study presented here attempts to efficiently and precisely map villages in the Indian sub-continent during floods via a three-stage approach which uses PolSAR data. However, an accurate segregation of villages in India even with PolSAR data is challenging because the built-up structures in the villages of rural India are closely placed and are randomly oriented w.r.t. each other. This condition either hinders their segregation or otherwise induces false alarms during extraction. More descriptive land cover characterization features and powerful feature classifiers may address this challenge. The study in this paper proposes a novel approach to efficiently detect and map flood affected villages which utilize polarization signatures from PolSAR imagery, ensemble-of -dilated-convolutions based CNNs, apriori knowledge and image morphology. The approach broadly involves three stages: first, built-up area extraction from a PolSAR image: second, detection of villages in a built-up area image and third, identification and mapping of villages that are affected by the flood. In the first stage, an ensemble of varying dilated-convolutions based novel CNN classifier which directly utilizes PolSAR-2 polarization signatures (PSs) in window-mode as features are developed to extract built-up areas. The second stage provides a novel village detection filter based on apriori knowledge and image morphology to detect actual villages and mask out the false objects. Finally, in the third stage, flood affected villages are mapped via a series of morphological operations based degree-of-intersection measure. Experiments are conducted on both simulated and natural flooded area datasets. Experimental results show 81% detection accuracy and 100% mapping performance of the proposed approach which indicates its potential as an effective flood affected village mapping system.

Finding a Needle in a Haystack: Faint and Small Space Object Detection in 16-Bit Astronomical Images Using a Deep Learning-Based Approach

With the increasing interest in space science exploration, the number of spacecraft in Earth’s orbit has been steadily increasing. To ensure the safety and operational integrity of active satellites, advanced surveillance and early warning of unknown space objects such as space debris are crucial. The traditional threshold-based filter for space object detection heavily relies on manual settings, leading to limitations such as poor flexibility, high false alarm rates, and weak target detection capability in low signal-to-noise ratios. Therefore, detecting faint and small objects against a complex starry background remains a formidable challenge. To address this challenge, we propose a novel, intelligent, and accurate detection method called You Only Look Once for Space Object Detection (SOD-YOLO). Our method includes the following novel modules: Multi-Channel Histogram Truncation (MHT) enhances feature representation, CD-ELAN based on Central Differential Convolution (CDC) facilitates learning contrast information, the Space-to-Depth (SPD) module replaces pooling layer to prevent small object feature loss, a simple and parameter-free attention module (SimAM) expands receptive field for Global Contextual Information, and Alpha-EIoU optimizes the loss function for efficient training. Experiments on our SSOD dataset show SOD-YOLO has the ability to detect objects with a minimum signal-to-noise ratio of 2.08, improves AP by 11.2% compared to YOLOv7, and enhances detection speed by 42.7%. Evaluation on the Spot the Geosynchronous Orbit Satellites (SpotGEO) dataset demonstrates SOD-YOLO’s comparable performance to state-of-the-art methods, affirming its generalization and precision.

Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection

In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High-energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN-based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.

Clutter Covariance Matrix Estimation for Radar Adaptive Detection Based on a Complex-Valued Convolutional Neural Network

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD).

PS-IPS: Deploying Intrusion Prevention System with machine learning on programmable switch

Intrusion prevention is significant to avoid device damage and financial losses. Researchers have proposed various Intrusion Prevention Systems (IPS) to prevent malware, including traditional and SDN-based IPS. However, existing IPSs suffer from low throughput problems caused by detection and rule-installation delays. Here, we propose a programmable switch-base IPS (named PS-IPS), which utilizes the switch CPU and pipeline to detect malware. PS-IPS consists of four main components: (1) parser, (2) flow filter, (3) recirculation director, and (4) malware detector. According to the experiment, PS-IPS achieves a 183X throughput than the SDN-based IPS. The response time of PS-IPS is also reduced by 99.99%, showing that PS-IPS effectively prevents malware with a single programmable switch.

A robust high-resolution potential-field edge detection filter with its application in the Indo-China Peninsula

Potential-field edge detection techniques are effective tools for identification of large-scale tectonic boundaries and geologic target bodies. Although a series of edge detectors have been developed, most of them suffer from a trade-off between edge resolution and noise immunity. To this end, we integrate multiple edge detectors such as normalized Harris filter (NHF) and ILTHG filter to propose a novel edge detector called ILGNH. The ILGNH edge detector takes advantage of both the enhanced noise immunity of NHF and the high resolution of ILTHG. Model experiments without/with noises both show that it is an alternative robust high-resolution edge detector. Afterwards, we apply this edge detection method to the Indo-China Peninsula and its adjacent areas to identify multi-phase tectonic boundaries. Combining the edge detection, residual crustal gravity anomalies (RCGA), and active faults, we identify four major NS transverse suture zones associated with microplates. These suture/boundary zones, together with deep faults, enclose 14 microplates with different interior feature of RCGA, while most of the boundaries are located in the gradient zones or the dislocation/distortion zones of the RCGA. Notably, inside the West Burma microplate, the ILGNH edge detection shows a distinct near NS-oriented arc-shaped deep boundary zone, which indicates that the underlying slab beneath the Indo-Burma Ranges was blocked during its eastward advance and shifted downward to form a high-angle deep subduction. Further, multi-scale edge detection at different depths confirms that the microplate boundary under the western Indo-China Peninsula vertically extends deeper than the eastern part. In addition, the seismicity below 50 km is concentrated to the west of the identified arc-shaped deep boundary zone and near the Naga-Kalaymyo-Arakan-Andaman Suture Zone.

Edge enhancement of potential field data using the enhanced gradient (EG) filter

Potential field data play a critical role in interpreting various geologic structural features through edge detection filters that aid in mapping subsurface structural features. For this purpose, various filters have been introduced in recent years to determine lateral boundaries. However, each of these filters has its limitations and advantages. This study presents a new edge enhancement filter named “Enhanced Gradient (EG)” based on the Richards function and applies it to potential field data for structural mapping. The EG is tested on two dimensional (2D) and three dimensional (3D) synthetic magnetic models with sources buried at different depths and variable properties. The results from the EG filter provide more accurate and higher resolution horizontal boundaries and can avoid creating the false edges in the output results. In addition, the proposed filter was examined using aeromagnetic data from the Indiana region in the USA. The primary and secondary faults and geological formations are recognizable in the EG image. The results of the EG map will allow us to improve the qualitative interpretation of potential field anomalies in studying the structural and tectonic geology of the Indiana region in the USA.

Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol.

This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.

Adaptive Technique for Contrast Enhancement of Leading Vehicle Tracks

During movement in various unpaved terrain conditions, the track impressions left over by the leading vehicles provide guiding and safe routes in the area. The delineation of these tracks captured by the images can extend immense support for guidance in real time. These tracks that look like edges in coarse-resolution images take the shape of elongated areas in fine-resolution images. In such a scenario, the high pass and edge detection filters give limited information to delineate these tracks passing through different surroundings. However, the distinct texture of these tracks assists in the delineation of these tracks from their surroundings. Gray level co-occurrence matrix (GLCM) representing the spatial relation of pixels is employed here to define the texture. The authors investigated the influence of different resolutions on the distinguishability of these tracks. The study revealed that texture plays an increasing role in distinguishing objects as the image resolution improves. The texture analysis extended to investigate the track impressions left over by the leading vehicle brings out an ample scope in delineating these tracks. The measures could improve the track contrast even better than conventional techniques. To select the most optimal contrast enhancement measure in a given scenario, authors proposed a quantified measure of track index. An investigation is made on the difference-based track index (TI) representing the mean contrast value of the track vis-à-vis off-track areas. The results show an increase in the quantified contrast from 7.83 per cent to 29.06 per cent. The proposed technique highlights the image with the highest track contrast in a given scenario. The study can lead to onboard decision-making for the rut following vehicles moving in low-contrast terrain.

Robust data-driven design of a jamming detection filter for airborne electromechanical actuators

The electrification of the actuations on aircraft is a technological trend towards a greener aviation. Electromechanical Actuators (EMAs) are a key item in the more electric aircraft concept. However, EMAs are still prone to the jamming of their mechanical transmission, leading to possible incidents when flight surfaces can no longer be controlled. Jamming detection algorithms are thus envisaged. In this paper, we conduct a large experimental campaign on a 1:1 scale airborne EMA and design a robust data-driven jamming detection algorithm. The designed algorithm is compared with a traditional non-robust model-based jamming detection algorithm design. The benefits of robust data-driven approach are experimentally evaluated on real faulty data with several performance indexes.

Distributed adaptive event-triggered fault detection filter of positive semi-Markovian jump systems

This paper presents a distributed fault detection filter (FDF) for positive semi-Markovian jump systems (PSMJSs) based on an adaptive event-triggered mechanism (AETM) scheme. A novel AETM is first proposed for the systems. A weighted fault model is introduced to assist the estimation of the original fault. Utilizing stochastic copositive Lyapunov function (CLF), a distributed FDF whose parameters are designed by virtue of linear programming (LP), is proposed. Under the designed filter, the positivity and stochastic stability with L1/L_-gain performance of a fault detection system is reached. The contribution of this paper contains three aspects: (i) An AETM is constructed for the considered systems, (ii) A distributed FDF is designed for the systems, and (iii) The hybrid L1/L_-gain performance is achieved with respect to the disturbance and the fault. Two illustrative examples are given for verifying the validity of the results.