Optimal Detection Parameters Research Articles

ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING

The document explores the application of the Canny Edge Detection method in facial recognition systems, specifically for identifying edge patterns in digital images. In the context of technological advancements, the focus is on enhancing data processing through efficient image analysis techniques. The research addresses how different edge detection methods, including Roberts, Prewitt, Sobel, and Canny, function, with the latter being highlighted for its superior ability to minimize error and deliver accurate edge detection results. The study outlines the development of a system designed to identify optimal edge detection parameters using the Canny method, focusing on facial images captured from the front. The system is limited to edge identification in such images, and performance is measured using a correlation coefficient. The process involves several technical steps, such as pre-processing the image (grayscale conversion and noise reduction) and using Gaussian filters and hysteresis thresholding to refine the detection. The research's ultimate aim is to optimize Canny's performance for identifying edges, contributing to advancements in facial recognition technology.

Computer Vision and Augmented Reality for Human-Centered Fatigue Crack Inspection.

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human-machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology.

Construction of nitrogen-doped carbon dots-based fluorescence probe for rapid, efficient and sensitive detection of chlortetracycline

Antibiotics are widely used in clinical medicine due to their excellent antibacterial abilities. As typical emerging pollutants, their misuse can lead to excess antibiotics entering the environment, causing antimicrobial resistance and leading to serious health problems via food chain. Herein, a nano-fluorescent probe based on nitrogen-doped carbon dots (N-CDs) was constructed for the sensitive detection of chlortetracycline (CTC). N-CDs with stable fluorescence were synthesized by hydrothermal method using alizarin red and melamine as raw materials. The N-CDs exhibited significant independence to excitation wavelength. The fluorescence of N-CDs was significantly quenched by CTC ascribing to the fluorescence resonance energy transfer mechanism. The concentration of N-CDs, solution pH and incubation time were optimized to obtain the optimal detection parameters. Under optimal conditions, CTC exhibited excellent linearity over the range of 20–1200 μg/L, and the detection limit was 8.74 μg/L. The method was validated with actual water samples and achieved satisfied spiked recoveries of 97.6–102.6%. Therefore, the proposed method has significant application value in the detection of CTC in waters.

High Precision Ultrasonic Testing Method for Density of Engineering Plastics

The density of engineering plastics is a key parameter for ensuring their safety and reliability. In order to achieve rapid and high-precision on-site detection, a method based on the acoustic pressure reflection coefficient is proposed. First, finite element simulation analysis was conducted to obtain the acoustic field distribution during ultrasound propagation under water immersion conditions. The correlation between interface echo intensity and material density was determined. Optimal detection parameters were designed to reduce measurement errors caused by beam overlap and diffusion attenuation. A water immersion ultrasonic experimental system was constructed, and the measurement accuracy of the method was tested using chlorinated polyvinyl chloride pipes. The results show that, compared to the measurement results of the Archimedean drainage method, the maximum error of ultrasonic measurements does not exceed 1.7%, and the overall variance is less than 1.2%. The measurement accuracy of this method is compared with the regression results of different machine learning models. It is demonstrated that, compared to regression methods based on variable correlation, this method retains the advantages of high efficiency and low cost in ultrasonic density measurement, while achieving higher measurement accuracy. Additionally, it does not require a dataset for training support, making it promising and valuable for practical applications.

Responsive neurostimulation of the thalamus improves seizure control in idiopathic generalised epilepsy: initial case series

ObjectivesUp to 40% of patients with idiopathic generalised epilepsy (IGE) are drug resistant and potentially could benefit from intracranial neuromodulation of the seizure circuit. We present outcomes following 2 years...

Scanning laser in-depth heating infrared thermography for deep debonding of glass curtain walls structural adhesive

For traditional infrared thermography (IRT) has a poor detection effect on deep defects, a novel infrared thermography method based on scanning laser in-depth heating was proposed to detect the deep debonding of hidden frame glass curtain walls structural adhesive which is located 6 mm underneath. The novel scanning laser in-depth heating thermography has advantages as follows: (1) scanning laser in-depth heating can heat the bonding interface of structure adhesive directly and has a greater detection capacity for deep debonding than surface heating; (2) the optimal detection scheme and parameters can get reasonable detection effect and prevent overheating which will cause structural adhesive performance degradation or damage; (3) thermal image reconstruction and integral enhancement can effectively improve detection effect and SNR of images. The test results demonstrated that the 10 mm × 8 mm debonding defect located 6 mm underneath form glass surface can be successfully detected.

Use of NDT systems to investigate pavement reconstruction needs and improve maintenance treatment decision-making

ABSTRACT The objective is to improve the accuracy of the test results and establish a maintenance treatment decision-making system using the non-destructive testing (NDT) technology. Firstly, the evaluation unit length was theoretically determined and statistically verified. Secondly, the test frequency of falling weight deflector (FWD) test was statistically analysed and the deflection acceptance criterionwas obtained based on the probability density distribution curve. Meanwhile, the relationship between the FWD deflection and the pavement internal damage obtained by drilling was analysed. Subsequently, the test parameters of the three-dimensional ground-penetrating radar (3D GPR) were optimised by using the non-reference image quality assessing technology. Followed, pavement internal damage index of GPR was proposed for locating the defect structural layer. Lastly, the decision-making systemwas established. Results showed that the suitable evaluation unit length is 20 m, the deflection test frequency is no more than 10 m/point, the deflection accept criterion is 29.5 (0.01 mm) with 95% reliability and the optimal detection parameters of GPR is 1 cm, 10 µs and 25 ns respectively. The established decision-making system fully considering the surface and the interior conditions as well as the functional and structural conditions can effectively guide the maintenance plan formulation for similar reconstruction and expansion projects.

Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

Evaluation of the Magnetocrystalline Anisotropy of Typical Materials Using MBN Technology.

Magnetic Barkhausen noise (MBN) signals in the stage from saturation to remanence of the hysteresis loop are closely correlated with magnetocrystalline anisotropy energy. MBN events in this stage are related to the nucleation and growth of reverse domains, and mainly affected by the crystallographic textures of materials. This paper aims to explore the angle-dependent magnetocrystalline anisotropy energy. Based on the consideration of macroscopic magnetic anisotropy, with the concept of coordinate transformation, a model was firstly established to simulate the magnetocrystalline anisotropy energy (MCE) of a given material. Secondly, the MBN signals in different directions were tested with a constructed experimental system and the characteristic parameters extracted from the corresponding stage were used to evaluate the magnetic anisotropy of the material. Finally, the microstructures of 4 materials were observed with a metallographic microscope. The microtextures of local areas were measured with the electron backscatter diffraction (EBSD) technique. The MBN experimental results obtained under different detection parameters showed significant differences. The optimal MBN detection parameters suitable for magnetic anisotropy research were determined and the experimental results were consistent with the results of MCE model. The study indicated that MBN technology was applicable to evaluate the MCE of pipeline steel and oriented silicon steel, especially pipeline steel.

Surface-enhanced Raman spectroscopy method for classification of doxycycline hydrochloride and tylosin in duck meat using gold nanoparticles

This paper investigated on 478 duck meat samples for the identification of 2 kinds of antibiotics, that is, doxycycline hydrochloride and tylosin, that were classified based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate techniques. The optimal detection parameters, including the effects of the adsorption time, and 2 enhancement substrates (i.e., gold nanoparticles as well as gold nanoparticles and NaCl) on Raman intensities, were analyzed using single factor analysis method. The results showed that the optimal adsorption time between gold nanoparticles and analytes was 2 min, and the colloidal gold nanoparticles without NaCl as the active substrate were more conducive to enhance the Raman spectra signal. The SERS data were pretreated by using the method of adaptive iterative penalty least square method (air-PLS) and second derivative, and from which the feature vectors were extracted with the help of principal component analysis. The first four principal components scores were selected as the input values of support vector machines model. The overall classification accuracy of the test set was 100%. The experimental results showed that the combination of SERS and multivariate analysis could identify the residues of doxycycline hydrochloride and tylosin in duck meat quickly and sensitively.

Modeling for Tracking Micro Gap Weld Based on Magneto-Optical Sensing and Kalman Filtering

Seam detecting and micro weld tracking are difficult in the laser welding process. In this paper, a micro gap weld with a width of not more than 0.05 mm is detected and identified by magneto-optical (MO) imaging technology. The MO imaging law of the micro gap weld under direct-current (DC) magnetic field excitation is studied, and the relationship between the gray-scale characteristics of the weld's MO image and the leakage magnetic field strength is analyzed based on the MO effect law. A finite element (FE) model of the micro gap weld is set up, and the leakage magnetic field distributions of different lift-off heights and different excitation voltages are analyzed, which is helpful in improving the detection accuracy of the center position of the micro gap weld. A MO imaging inspection system for micro gap weld excited by a DC magnetic field is established. According to the finite element simulation results and the MO imaging experiments, the optimal excitation voltage of the MO imaging device is 15 V, and the optimal lift-off height is 1.0 mm. To reduce the influence of noise coupled with the laser welding process, the measurement precision of the micro gap weld center was improved by the Kalman filtering (KF) method. The experimental results at different welding speeds indicated that MO imaging can be used for high-precision micro gap weld center identification under optimal detection parameters.

Optimal automatic detection of muscle activation intervals

A significant challenge in surface electromyography (sEMG) is the accurate identification of onsets and offsets of muscle activations. Manual labelling and automatic detection are currently used with varying degrees of reliability, accuracy and time efficiency. Automatic methods still require significant manual input to set the optimal parameters for the detection algorithm. These parameters usually need to be adjusted for each individual, muscle and movement task. We propose a method to automatically identify optimal detection parameters in a minimally supervised way. The proposed method solves an optimisation problem that only requires as input the number of activation bursts in the sEMG in a given time interval. This approach was tested on an extended version of the widely adopted double thresholding algorithm, although the optimisation could be applied to any detection algorithm. sEMG data from 22 healthy participants performing a single (ankle dorsiflexion) and a multi-joint (step on/off) task were used for evaluation. Detection rate, concordance, F1 score as an average of sensitivity and precision, degree of over detection, and degree of under detection were used as performance metrices. The proposed method improved the performance of the double thresholding algorithm in multi-joint movement and had the same performance in single joint movement with respect to the performance of the double thresholding algorithm with task specific global parameters. Moreover, the proposed method was robust when an error of up to ±10% was introduced in the number of activation bursts in the optimisation phase regardless of the movement. In conclusion, our optimised method has improved the automation of a sEMG detection algorithm which may reduce the time burden associated with current sEMG processing.

Optimal Methodology for Detecting Land Cover Change in a Forestry, Lakeside Environment Using NAIP Imagery

Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using one-meter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments.

Noncooled (Hg,Cd)Te IR photo detectors in range of 10.6 μm wavelength

In range of 10.6 μm IR radiation an analysis of noncooled (T = 300 K) (PC) photodiodes and (EMCD) magnetoexlusion detectors with (Hg,Cd)Te was made. The basic detection parameters of these devices are limited by the noise resulting from statistical processes of thermal generation and carrier recombination. The parameter evaluation of related photodiodes demonstrates that the optimal parameters such as: thickness or structure doping on the substrate with a widened energy gap differs each other for structures with ohmic contacts at both ends. In the paper, for photodiodes and magnetoexclusion detectors with (Hg,Cd)Te the research results : optimal photosensitive detection parameters are presented for the room temperature (T=300 K) and acceptable large as well as small (5 and 0.5 W / mm2) densities of the dissipated power.

Optimization of a Precolumn OPA Derivatization HPLC Assay for Monitoring of l-Asparagine Depletion in Serum during l-Asparaginase Therapy.

A method for monitoring l-asparagine (ASN) depletion in patients' serum using reversed-phase high-performance liquid chromatography with precolumn o-phthalaldehyde and ethanethiol (ET) derivatization is described. In order to improve the signal and stability of analytes, several important factors including precipitant reagent, derivatization conditions and detection wavelengths were optimized. The recovery of the analytes in biological matrix was the highest when 4% sulfosalicylic acid (1:1, v/v) was used as a precipitant reagent. Optimal fluorescence detection parameters were determined as λex = 340 nm and λem = 444 nm for maximal signal. The signal of analytes was the highest when the reagent ET and borate buffer of pH 9.9 were used in the derivatization solution. And the corresponding derivative products were stable up to 19 h. The validated method had been successfully applied to monitor ASN depletion and l-aspartic acid, l-glutamine, l-glutamic acid levels in pediatric patients during l-asparaginase therapy.

Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms.

Error analysis of the crystal orientations obtained by the dictionary approach to EBSD indexing

The efficacy of the dictionary approach to Electron Back-Scatter Diffraction (EBSD) indexing was evaluated through the analysis of the error in the retrieved crystal orientations. EBSPs simulated by the Callahan-De Graef forward model were used for this purpose. Patterns were noised, distorted, and binned prior to dictionary indexing. Patterns with a high level of noise, with optical distortions, and with a 25 × 25 pixel size, when the error in projection center was 0.7% of the pattern width and the error in specimen tilt was 0.8°, were indexed with a 0.8° mean error in orientation. The same patterns, but 60 × 60 pixel in size, were indexed by the standard 2D Hough transform based approach with almost the same orientation accuracy. Optimal detection parameters in the Hough space were obtained by minimizing the orientation error. It was shown that if the error in detector geometry can be reduced to 0.1% in projection center and 0.1° in specimen tilt, the dictionary approach can retrieve a crystal orientation with a 0.2° accuracy.

High sensitivity HPLC method for analysis of in vivo extracellular GABA using optimized fluorescence parameters for o-phthalaldehyde (OPA)/sulfite derivatives.

Reversed-phase HPLC with derivatization using o-phthalaldehyde (OPA) and sulfite allows electrochemical detection of γ-aminobutyric acid (GABA) in microdialysis samples. However, OPA/sulfite derivatives have been reported to produce lower fluorescent yield than OPA derivatives using organic thiols as the nucleophile. To overcome this limitation we examined excitation and emission spectra, reaction time, pH, and concentration of reagents in the derivatization solution. Optimal detection parameters were determined as λex=220nm and λem=385nm for maximal fluorescence. The derivatization reaction occurred immediately and the product was stable up to 24 h [corrected]. A pH of 10.4 for the borate buffer used in the derivatization solution was significantly better than lower pH. Increasing the amount of sulfite combined with diluting the derivatization solution in borate buffer resulted in complete separation of the GABA peak from contaminants without any loss in signal. Controlling the temperature of the detector at 15°C significantly improved sensitivity with a detection limit of approximately 1nM. To validate this assay, we performed microdialysis in the dorsal striatum and ventral tegmental area (VTA) of adult Long Evans rats. GABA concentrations in dialysates were determined using external standards and standard additions, in order to further confirm interfering peaks were not present in biological samples. Within the dorsal striatum (n=4), basal GABA concentrations were 12.9±2.2 and 14.5±2.2nM (external and additions, respectively). Respective basal GABA concentrations in the VTA (n=3) were 4.6±1.1 and 5.1±0.6nM. Thus, we have developed a novel, sensitive fluorescence method to determine GABA in microdialysates using HPLC of an OPA/sulfite derivative.

Image forgery detection using segmentation and swarm intelligent algorithm

Small or smooth cloned regions are difficult to be detected in image copy-move forgery (CMF) detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent (SI) algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform (SIFT)-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.

Detection of crackle events using a multi-feature approach.

The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy. The classification was done using a logistic regression classifier. Data from seventeen patients with manifestations of adventitious sounds and three healthy volunteers were used to evaluate the performance of the proposed method. The dataset includes crackles, wheezes and normal lung sounds. The optimal detection parameters, such as the number of features, were chosen based on a grid search. The performance of the detection was studied taking into account the sensitivity and the positive predictive value. For the conditions tested, the best results were obtained for the frame size equal to 128 ms and twenty-seven features.