A Comparison of Sobel and Prewitt Edge Detection Operators

Detection of edge is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristics by object recognition. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. So, edge detection is a vital step in image analysis and it is the key of solving many complex problems. This paper, describes edge detection algorithms for image segmentation using various computing approaches which have got great fruits. Experimental results prove that Canny operator is better than Prewitt and Sobel for the selected image. Subjective and Objective methods are used to evaluate the different edge operators. The performance of Canny, Sobel and Prewitt Edge Detection are evaluated for detection of edges in digital images.

In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flame edges in digital images with a timely manner, which has a huge impact on saving life and reducing loss of life.

The subject of Detecting edges in images is considered one of the main topics in digital image processing and the most common one, due to its wide applications in many fields. Classical methods for detecting edges in digital images still give excellent results if the threshold is chosen correctly. In this paper, a group of classical edge algorithms was taken and tested on different types of images, Canny edge detection algorithm gave the best results in all circumstances if the threshold value of it was set between 0.30-0.45. The range of the threshold values was from 0.1 to 0.45 in Roberts, Sobel, and Prewitt's edge detection algorithms. In this paper, four famous classical algorithms were tested on some standard RG BA images which had some noises by purpose and by holding different frequencies, containing different types of noise. Since the number of images has reached 50 thousand in total, a lot of data has been obtained and these algorithms have been tested on a large number of images. Indicating the algorithms implemented to perform on different image types, the threshold value was changed from 0 to 1 thousand times with each image by 0.001 value.

The objective of the present study is to investigate systematically the key metrics to evaluate the life extension performance of offshore wind farm operations. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. The analyzed key performance metrics are gross profit margin, return on asset, compounded annual rate of return of initial investment and levelized cost of energy. The mean value and standard deviation of each performance metric are calculated within a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by a multi-asset portfolio optimization. The Markowitz modern portfolio theory is applied to estimate the maximum risk-adjusted ratio and Sharpe ratio, for the key performance metrics. Subsequently, the key performance metrics are compared to identify the most suitable metrics at different stages of the life extension. Moreover, the present study investigates the effect of different uncertainty levels associated with the stochastic variables in the techno-economic assessment. Finally, the suitability of performance metrics is analyzed and discussed for different offshore wind farm sizes and related recommendations are given.

Steganography offers completely invisible and secure communication that relies on careful embedding so that it is impossible for even keen observers to detect hidden data. In this work, we propose an enhanced edge-based framework that brings together image segmentation, Canny edge detection, and a 128-bit seeded pseudo-random number generator (PRNG) to drive randomized least significant bit (LSB) substitution. Edge detection plays a critical role in identifying regions of significant intensity variation in digital images, which can be leveraged to enhance the imperceptibility and robustness of steganographic techniques. This paper proposes an improved edge-based steganographic method that integrates image segmentation with randomized LSB substitution to embed confidential data securely. The approach begins by segmenting the input image and extracting edge regions using the Canny edge detector. These edge pixels are then randomized using a 128-bit pseudo-random key to determine the embedding locations, ensuring unpredictability and increased security. To maintain high visual fidelity, the secret message is divided into uniform-length binary segments and embedded only within the selected edge pixels, thereby minimizing distortion. The proposed method is evaluated using key performance metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), embedding capacity, and computational efficiency. Experimental results on standard benchmark images demonstrate high imperceptibility (average PSNR exceeding 65 dB and SSIM approaching 1.00), low embedding time, and strong resistance to statistical detection. Histogram analysis and ablation studies further confirm the visual and statistical invisibility of the stego-images. Overall, the method offers a robust, secure, and efficient solution for data hiding in digital images.

Real-time image processing applications requires processing on large data of pixels in a given timing constraints. Reconfigurable device like FPGAs have emerged as promising solutions for reducing execution times by deploying parallelism techniques in image processing algorithms. Implementation of highly parallel system architecture, parallel access of large internal memory banks and optimization of processing element for applications makes FPGA an ideal device for image processing system. Edge detection is basic tool used in many image processing applications for extracting information from image. Sobel edge detection is gradient based edge detection method used to find edge pixels in image. This paper presents a design of a Sobel edge detection algorithm to find edge pixels in gray scale image. Xilinx ISE Design Suite-14 software platforms is used to design a algorithm using VHDL language. MATLAB software platform is used for obtaining pixel data matrix from gray scale image and vice versa. Xilinx FPGAs of family Vertex-5 are more suitable for image processing work than Spartan-3 and Spartan-6.

Radar is the most eminent device in the prolonged scattering era The mechanisms involve using electromagnetic waves to take Synthetic Aperture Radar (SAR) images for long reaching. The process of setting edges is one of the important processes used in many fields, including radar images, which assists in showing objects such as mobile vehicles, ships, aircraft, and meteorological and terrain forms. In order to accurately identify these objects, their edges must be detected. Many old-style methods are used to isolate the edges but they do not give good results in the determination process. Conservative methods use an operator to detect the edges, such as the Sobel operator which is used to perform edge detection where the edge does not appear well.
 The proposed method which combines Ridgelet transform, Bezier curve and Sobel operator is used to detect edges very efficiently. Ridghelet transform resolves the harms in the wavelet transform and it can well detect the edges in images. Bezier curve can profit gradual variation of the data and their mutability. Hence, the efficiency of the edged image is improved and, when used with Sobel operator, the quality of the edge image become very good. The data show that the advocated method has superior fallouts over the Sobel edge detection and the wavelet method in both subjective and impartial experiments. While the Peak Signal to Noise Ratio(PSNR) values were equal to 9.3812, 9.8918, 9.6521 and 9.0743using the Sobel operator method and to10.2564, 10.7927, 10.5612and 10.8633 using the wavelet method, they were increased in the proposed method to 12.6542, 12.9514, 12.8574 and 12.3013 respectively.

In this study, we conducted a comprehensive DC and AC analysis of Gate-All-Around (GAA) devices, focusing on the implications of increased gate capacitance both for wider and narrower device on key performance metrics. Our findings reveal that the elevated gate capacitance due to thin GaN cap layer significantly affects the transconductance (gm), Drain-Induced Barrier Lowering (DIBL), subthreshold swing (SS) and the on/off current ratio (Ion/Ioff). A new analytical linkage is formulated connecting gate capacitance ( C g ) with key DC performance metrics. The results indicated a significant enhancement in both ft and fmax, attributed to the high Ion/Ioff ratio of 1.72 × 10 13 , confirming the superior high-frequency performance of GAA structures. Our analysis underscores the critical role of gate capacitance in optimising the electrical characteristics of GAA devices, paving the way for their application in 5G wireless communication.

IntroductionRecruitment and retention remain critical challenges in clinical trials, particularly in neurodegenerative diseases, which require large participant populations, rigorous screening, and prolonged follow-up periods. Care Access is a global research site management organization that operates clinical trial sites employing various operational models. This study evaluates the operational performance of Care Access site models—including traditional sites, hub-and-spoke, and decentralized community-integrated research (DCIR) sites—within a Phase 3 neurodegenerative disease trial, focusing on their relative efficiency in recruitment, randomization, and retention. The inclusion of multiple site models within the same trial presents a rare opportunity for direct comparison under uniform study conditions, providing unique insights into their respective advantages and challenges. By analyzing key site performance metrics and the role of innovative operational strategies, this study aims to identify effective approaches to enhancing trial efficiency and overcoming recruitment challenges to inform the design and conduct of future trials.MethodsThe trial involved 32 Care Access sites each employing one of these distinct operational models. Key performance metrics, such as participant screening rates, randomization rates, screen failure rates, and post-randomization discontinuation rates, were analyzed across (a) traditional, (b) hub-and-spoke, and (c) DCIR site models. We also compared the enrollment performance of Care Access to that of 196 non-Care Access sites using publicly available data.ResultsDCIR Sites demonstrated the highest recruitment efficiency, screening 20.61 participants per site per month and randomizing 0.79 participants per site per month, compared to 11.78 and 0.50 for traditional sites, and 12.20 and 0.45 for hub-and-spoke sites, respectively. Despite being newly established, and operating in a decentralized model, DCIR sites achieved post-randomization discontinuation rates (28.17%) comparable to those of traditional site models (26.28%), highlighting their effectiveness in maintaining participant engagement. All site models encountered high screen failure rates (~95%), consistent with Phase 3 trials for neurodegenerative diseases. Notably, a community-engaged, research-only facility achieved the lowest discontinuation rate (17.65%) among all sites, highlighting the potential of strong local engagement to significantly enhance retention and participation. Furthermore, when comparing Care Access sites with non-Care Access sites in this trial, Care Access sites achieved an average randomization rate of 15.6 participants per site, outperforming the 8.7 participants per site recorded by non-Care Access sites. Data quality, monitoring practices, and overall data integrity were consistent across all site models, supporting the reliability of findings across both decentralized and traditional approaches. This comparison highlights the effectiveness of the innovative operational framework and decentralized community engagement approach in overcoming traditional recruitment challenges and enhancing trial outcomes.DiscussionDCIR sites exhibited superior participant screening and randomization efficiency while maintaining discontinuation rates comparable to traditional site models. This success was driven by a combination of innovative operational strategies, including decentralized community-based outreach mechanisms that expanded population access to research by bringing trials directly to populations that previously lacked access to clinical research. At the same time, this approach helped reach underrepresented groups, thereby improving both geographic coverage and trial generalizability while enhancing overall trial performance. Additionally, other innovations like the deployment of centralized remote research coordinators also played a role by streamlining remotely-conducted tasks, allowing site staff, in all site models, to focus on participant care and engagement. These findings highlight the effectiveness of a flexible, multi-model site strategy in addressing recruitment and retention challenges in large-scale Phase 3 neurodegenerative disease trials and suggest that this approach may extend to other therapeutic areas facing similar challenges.

In 2017, Uber announced it had a “broken relationship” with its drivers. Yet what broke the relationship and what effect might it have on Uber’s organizational performance? This paper is a mixed-methods study of how conflict impacts the relationship between platforms and drivers. First, by drawing on 55 original interviews with rideshare drivers, this paper identifies conflict triggers that damage the relationship between platforms and drivers. Second, this paper uses new time diary data from 490 Uber, Lyft, Juno, and other Transportation Network Company (TNC) drivers from across the United States to empirically test if these conflict triggers are associated with key TNC performance metrics. Specifically, this paper finds that a “broken relationship” is associated with drivers withholding their working time from Uber and allocating it toward their competitor, Lyft. Additionally, this paper finds that drivers who have a broken relationship with Uber are more likely to recruit (‘steer’) passengers toward Lyft. These behaviors provide a link between organizational conflict and key performance metrics in the ‘gig economy.’

The performance of optoelectronic devices is affected by various noise sources. A notable factor is the 4.2% lattice mismatch at the Ge/Si interface, which significantly influences the efficiency of Ge-on-Si photodetectors. These noise sources can be analyzed by examining the impact of the Ge/Si interface and deep traps on dark and photocurrents. This study evaluates the impact of these charge traps on key photodetector performance metrics, including responsivity, photo-to-dark current ratio, noise equivalent power (NEP), and specific detectivity (D*). The trapping effects on charge transport under both forward and reverse bias conditions are monitored through hysteresis analysis. When illuminated with an unmodulated 1550 nm laser, all the key performance metrics exhibit maximum variations at a specific reverse bias. This critical bias marks the transition from saturated to exponential charge transport regimes, where intensified electric fields enhance trap-assisted recombination and thus maximize metric fluctuations.

Objectives:The primary objective was to evaluate the impact of ulnar collateral ligament reconstruction (UCLR) on MLB pitcher performance, with a specific focus on advanced metrics such as fastball velocity, spin rate, FIP (fielding independent pitching), SIERA (skill interactive earned run average), and WHIP (walks and hits per inning pitched) when compared to both pre-injury performance and a non-injured cohort of pitchers. The secondary objective was to assess the extent to which velocity and spin rate are predictive of pitcher performance. The hypotheses for the primary objective was that there would be no difference in pre-injury and post-injury performance or in post-injury performance and healthy controls. The hypothesis for the secondary objective was that both velocity and spin rate would be predictive of performance regardless of injury status.Methods:Pitchers with confirmed UCL injuries between the 2017 and 2021 MLB seasons were identified using the Pro Sports Transactions Archive and baseball-reference.com. Inclusion criteria required pitchers to have thrown at least 8.0 innings in two consecutive seasons both pre- and post-injury. A control group of healthy pitchers was age-matched at a 1:2 ratio to injured pitchers (Fig 1). Key performance metrics, including FIP, SIERA, and WHIP, were extracted from fangraphs.com, and spin rate and velocity data were collected from Baseball Savant.Principal component analysis (PCA) was used to compress several pitching performance metrics (FIP, SIERA, WHIP) into a single, comprehensive performance measure, referred to as the first principal component (PC1), where lower values indicate better overall performance. The changes in performance before and after surgery were normalized to account for age-related decline, which was controlled for by using an age-matched group of healthy control pitchers. Pearson’s correlation coefficients were calculated to assess the relationship between spin rate, velocity, and performance. Comparisons between pre- and post-surgery performance and between injured and control pitchers were conducted using independent t-tests. A power analysis was conducted to ensure a sufficient sample size to detect meaningful differences in performance outcomes, and all analyses were performed using Python 3.7 and RStudio 2023. Statistical significance was set at α = 0.05.Results:The study included 34 MLB pitchers who had undergone UCLR, with an average age at the time of injury of 27.03 ± 3.05 years. Age-matched controls (n=68) were identified for comparison, allowing for the analysis of both performance differences and the potential influence of aging on pitching metrics. Performance was first analyzed in terms of pitching volume (number of pitches and innings pitched). While the injured group showed a slight decline in innings pitched post-surgery, this difference was not statistically significant when compared to the control group (p = 0.301). Furthermore, no significant differences were found in strikeouts (p= 0.992) or hits allowed (p= 0.207) at two seasons before versus after injury. The PC1 analysis of FIP, SIERA, WHIP revealed no significant difference between injured pitchers post-surgery compared to their control counterparts (p = 0.287) (Fig 2).Spin rate and velocity were further analyzed to determine their relationship with post-surgical performance. Fastball velocity showed no significant change post-surgery (p = 0.687), and spin rate also did not significantly differ between injured and control pitchers (p = 0.876). However, both spin rate and velocity were identified as a key predictors of performance for both groups for WHIP (pspin= 0.02, pvelo= 0.04), FIP(pFIPspin= 0.003, pFIPvelo< 0.001), SIERA (pSIERAspin< 0.001, pSIERAvelo< 0.001), and PC1 (pPC1spin< 0.001, pPC1velo< 0.001) (Fig 3). There were no significant relationship between age at the time of injury and changes in performance, spin rate, or velocity, indicating that age did not influence post-operative outcomes (Pearson’s r = -0.072, p = 0.685).Conclusions:Following UCLR, MLB pitchers maintain their pre-injury level of performance, with no significant decline in fastball velocity, spin rate, or advanced statistics post-surgery. Additionally, both spin rate and velocity emerged as significant predictors of pitching performance across both injured and healthy pitchers, highlighting their potential utility as a key metric in assessing pitching effectiveness. These findings support the conclusion that UCLR is an effective intervention for MLB pitchers, allowing them to return to competitive play without significant deterioration in their key performance metrics.

Ant colony optimization is a swarm intelligent algorithm that mimics the ant behaviors to optimize solutions for hard optimization problems. Over years Ant-based algorithms have been used in solving different problems including: Traveling Salesman Problem (TSP), Wireless Sensors Network (WSN), Benchmark Problem, and it has been used in various image processing applications. In the image processing fields various techniques have been used to detect edges in a digital image such as Canny and Sobel edge detectors. This Study, proposed a hybridized Ant Colony Optimization algorithm for optimizing the edge detector quality. The proposed method initializes its attribute matrix and the information at each pixel routed by ants on the input image. Experimental results show the results of the proposed algorithm and compare the results with the original built-in MATLAB edge detection method called Canny and the results of basic Aco edge detector. All three algorithms tested in different images and the MSE and PNSR are calculated before and after applying Gaussian noise. Based on the Experimental results obtained by the three used methods (Canny Edge Detector, Ant Colony Optimization, and Hybrid Aco-Canny), the proposed Hybrid ACO-CANNY methods was the best method for detecting edges.

Background. Badminton, a racquet sport that has gained global popularity, demands technical precision, tactical awareness, and exceptional physical fitness. Skills such as smashing, footwork, and minimizing errors are critical to success. However, the specific influence of age and gender on these metrics, especially among younger players, remains underexplored. Objectives. This study aimed to examine the effect of age and gender on key badminton performance metrics, including smash ability, footwork, and unforced errors, in order to identify developmental and demographic factors influencing skill acquisition and execution. Materials and methods. A quantitative descriptive study involved 24 athletes (aged 9–14) from the Wincorp badminton organization in Surakarta, Indonesia. Participants were grouped by age (9–10, 11–12, 13–14 years) and gender, ensuring equal representation. Over two months, data on smashing, lobbing, driving, footwork, and error rates were collected. Descriptive statistics and MANOVA analyzed differences, with a significance level set at p &lt; 0.05. Results. MANOVA revealed significant age-related effects on smashing (p = 0.000), footwork (p = 0.000), and error points (p = 0.000), with beginners (13–14 years) excelling in most metrics. Gender differences were also found to be substantial for smashing (p = 0.000), footwork (p = 0.000), and error points (p = 0.003), with males outperforming females in most categories. Interaction effects between age and gender were significant for smashing and footwork (p &lt; 0.05). However, no considerable differences were observed for netting and serving strokes across age or gender. Conclusions. The study indicates that age and gender significantly influence badminton performance metrics. Beginner athletes (13–14 years) demonstrated superior skills compared to younger groups, while males generally outperformed females. These findings highlight the importance of tailoring training programs by age and gender to optimize skill development and reduce performance gaps. Further studies should be performed to investigate biomechanical and psychological factors to refine coaching strategies.

The iris is a part of the human anatomy that can be used as a biometric identifier. Data obtained from the iris can be converted into information through iris image processing, and in order to obtain accurate iris pixel results, an edge detection operator is required that can provide detailed and good image quality effects. In this research, a comparative analysis of the Canny, Sobel, Prewitt and Roberts edge detection operators was carried out on iris images. The purpose of performing a comparative analysis of edge detection methods is to compare the detection results of each edge detection operator on iris recognition detected by each operator. The results of the comparison of edge detection methods using precision tables can be analyzed to show that the Canny edge detection operator provides better, smoother and sharper edge results in actual edge point detection, namely 0.357867, while Sobel =, 0.210212, Prewitt = 0.212452 and Roberts = 0.279196. From these results it can be concluded that the edge detection results provided by Sobel and Prewitt are less sharp and sensitive to noise, and the comparison results can vary depending on the intensity of the image and the image object being compared.