Adaptive Edge Detection Method for Image Polluted Using Canny Operator and Otsu Threshold Selection

For improving the processing speed and accuracy of edge detection, an adaptive edge detection method based on improved NMS (nonmaximum suppression) was proposed in this paper. In the method, the gradient image was computed by four directional Sobel operators. Then, the gradient image was processed by using NMS method. By defining a power map function, the elements values of gradient image histogram were mapped into a wider value range. By calculating the maximal between-class variance according to the mapped histogram, the corresponding threshold was obtained as adaptive threshold value in edge detection. Finally, to be convenient for engineering application, the proposed method was realized in FPGA (Field Programmable Gate Array). The experiment results demonstrated that the proposed method was effective in edge detection and suitable for real-time application.

The Wigner distribution (WD) produces highly concentrated time-frequency (TF) representation of nonstationary signals. It may be used as an efficient signal analysis tool, including the cases of frequency modulated signals corrupted with the Gaussian noise. In some applications, a significant amount of impulse noise is present. Then, the WD fails to produce satisfactory results. The robust periodogram has been introduced for spectral estimation of this kind of noisy signals. It can produce good concentration for pure harmonic signals. However, it is not so efficient in the cases of signals with rapidly varying frequency. This is the motivation for introducing the robust WD. It is a reliable TF representation tool for wide class of nonstationary signals corrupted with impulse noise. This distribution produces good accuracy of the instantaneous frequency (IF) estimation. Using the Huber (1981) loss function, a generalization of the WD is presented. It includes both the standard and the robust WD as special cases. This distribution can be used for TF analysis of signals corrupted with a mixture of impulse and Gaussian noise. The presented theory is illustrated on examples, including applications on the IF estimation and time-varying filtering of signals corrupted with a mixture of the Gaussian and impulse noise. The case study analysis of the IF estimators' accuracy, based on the standard and the robust WD forms, is performed. In order to improve the IF estimation, a median filter is applied on the obtained IF estimate.

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

The multiuser detection is a signal processing tech- nique which enhances the performance and capacity in DS- CDMA technology. Since its complexity issue was too expensive to handle, there was focus on the suboptimal detectors. In this paper we propose a hybrid method called adaptive differential interference cancellation method (ADIC) which is a hybrid of the differential interference cancellation method and which is then combined in a adaptive way to determine a weight matrix which will map the outcome of the differential interference cancellation method to come out with a better performance. This paper also looks into the computational cost of different methods using the floating point operation (flops) which is a good estimation of the comparative computational cost if not the exact one. I. INTRODUCTION The conventional DS-CDMA match filter detector fails to combat these issues due three prominent drawbacks: unrealis- tic ucorrelated code, near-far problem (even with strict power control (1)), and tough multiple access interference (MAI). Many advanced signal processing approaches have been made to improve the performances of these kinds of detectors. In this paper we will study the different approaches that were made in this field and implement these methods to reach a conclusion about the detection methods. As the name of the topic suggests the focus of our work will be study and propose cost effective better performing multiuser detection method. We will explore Multiuser Detection Techniques in Synchronous CDMA environment. Multi-user detection deals with the development and ap- plication of joint demodulation and interference cancellation cancellation techniques for improved detection of a desired set of digital signals in the presence of multiple access interfer- ence, intersymbol interference and noise. Since veterbi forward dynamic algorithm (2) is used in case of Optimum multiuser detection (3) the complexity grows in with the increase in the number of users. Hence the complexity is immense which increases exponentially with the increase in the number of users. Looking ahead for lower complexity hence lesser cost of implementation, the paper proposes the use of sub-optimal linear detectors (4). The sub-optimal detector the paper next focuses on is Decorrelator type of Detector (5) which is optimal in least squares. This detector out performs the optimal detector in many respects. Then the Minimum Mean Square Error (MMSE) linear detector is proposed which tries to minimize the squared error from the required transformation. It is one of the most popular detectors in use due to its easiness to implement and its lower complexity. But then in the above detection methods the decisions made were based on certain linear transformation criterion and there are missing efforts to check the performance in between the detection process to improvise the transformation method better detection. So then the paper proposes Adaptive multiuser detection. Then the paper focuses on Interference cancellation methods which utilize the feedback to reduce Multiple Access Interference (MAI) in the detected bits. Under the Interference Cancellation umbrella the Successive Interference Cancellation (SIC) (6), (7) method is explored and simulated to observe the results. Then the next method which performs better than SIC and more commonly used in real life, called Parallel Interference Cancellation (PIC) (6), (8) is also implemented. Before going ahead to implement the final proposed detection method, this paper implements Differential Interference Cancellation (DIC) (9) method. And then this paper finally proposes an Inter- ference Cancellation method called the Adaptive Differential Interference Cancellation Multiuser Detection method which is cost effective in comparison to other Interference Cancellation methods as we will require lesser hardware (multipliers as in the SIC or Parallel Interference Cancellation (PIC)) for that matter. More ever since we are using adaptive method the desired BER can be further reduced. This proposed method will be discussed in detail in the following section.

It is first elaborated that when the transmission time of underwater acoustic waves is estimated from the transmitting end to the receiving end, LFM pulse signal is often used, because after it is processed through matched filtering, a very important phenomenon will produce, which is pulse compression that a wide pulse in the time dimension is squeezed into a narrow pulse, so that transmission time can be accurately estimated; secondly it is explained that due to the multipath effect in the shallow water, reverberation signal in the propagation process and environmental noise and other factors, when a common detection method of fixed threshold is used, often the wrong detection can be caused, an adaptive detection method is presented for this problem, that is based on the pulse compression of the LFM signal, and the adaptive detection algorithm, the correlation peak of sound waves reach time is detected; finally through processing simulated data and measured data of the sea, it is verified that the this method is effective. Introduction It is a commonly used method of underwater acoustic positioning that the beacon is installed in the cooperation of underwater target, using acoustic path difference or phase difference from sound waves to the measuring array to locate[1]. But we know, in the communication process of actual ocean channel, signal detection and processing is a very important step, especially under the circumstances of the presence of multipath effect in shallow water, reverberation signal in the propagation process and the presence of environmental noise, it is very key how to accurately obtain the correlation peak of direct sound. In practical applications LFM signal is often used, we can first use the cross-correlation method of same frequency correlation and different frequency uncorrelation to filter the signal, in order to remove the interference effects of the noise, after signal is processed by matched filtering, a very important phenomenon will produce, that is pulse compression that a wide pulse in the time dimension is squeezed into a narrow pulse, so transmission time can be accurately estimated[2][3]. But in the actual ocean channel it is not easy to effectively detect the correlation peak, using common detection method of fixed threshold, wrong detection is offen generated. So this paper an adaptive detection method is proposed, which can effectively detect the correlation peak. Through the simulation and sea trial data, it is verified that this method is good and has certain practical value. Analysis of LFM pulse signal characteristics Mathematical expression of a linear frequency modulated pulse signal is given in Formula (1)[4], and its waveform is shown in Figure 1.    < < + = others T t t K t f j A t s L LFM 0 0 )]] * 2 / ( 2 exp[ Re[ ) ( 2 p (1) International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 290 Fig.1 LFM signal time-domain waveform It is a rectangular pulse that envelope width is T and frequency linearly changes with time. Among them, T f f K H L / ) ( − = is change rate of the signal frequency, or frequency rate, LFM signal FM range is ] , [ H L f f . After the LFM pulse signal is treated through self correlation matched filtering, a very important phenomenon will produce, that is pulse compression that a wide pulse in the time dimension is squeezed into a narrow pulse. The following is the analysis of this nature: the envelope of autocorrelation function absolute value of LFM pulse signal is given in Formula (2).      < < − − = others T t T K T K A RS 0 )] ( sin[ 2 ) ( 2

This paper presents a foot-mounted inertial sensor system for pedestrian localization, and aims to find an adaptive stance detection method for pedestrian gait analysis. The approach is based on a Support Vector Machine (SVM) classifier, which divides the gaits into two types: walking and running. For walking, the algorithm uses two threshold conditions and a median filter to detect stance and still phases. For running, a new step detection method based on Extended Kalman Filter (EKF) is used to roughly identify every step of running at first, and then empirical formulas are summarized between the average velocity of each step and thresholds. The corrected thresholds based on empirical formulas are used in the second-round accurate stance detection. The localization accuracy for running is largely improved in this algorithm.

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level.

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.

CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion

In this paper, an adaptive change detection method for polarization synthetic aperture radar (Pol-SAR) images change detection in low SNR circumstances is presented. It is difficult to detect the changed targets in strong noise or interference circumstance. Original coherent change detection method is invalid, because of many of false-alarm targets appear. Firstly, we analyze the distribution characteristic of coherence parameters. And then, we find that a kind of coherence parameters of Pol-SAR images can be proved following Gaussian distribution. Finally, an adaptive judgment criterion was used to detect the change targets. The experiment shows that the proposed method has a better detection performance.

Novel adaptive fault detection method based on kernel entropy component analysis integrating moving window of dissimilarity for nonlinear dynamic processes

Under the dual constraints of strong industrial noise and small sample size, achieving high precision and reliability in pipeline leakage detection is a significant challenge. The root cause lies in the fact that traditional single-modal methods have a single perception dimension and insufficient anti-interference ability, making it difficult to effectively distinguish strong background noise with overlapping spectra, resulting in reduced measurement accuracy and reliability. To address this issue, this paper proposes an adaptive noise reduction detection method based on multi-modal Transformer and contrastive learning (MDT-CL). This paper uses pressure, vibration, and acoustic emission sensors to capture complementary signals, and synchronizes the collection of these signals through dynamic time warping technology. The proposed method comprises three key innovations. Firstly, we propose a multi-scale Transformer encoder based on physical principles, employing an asymmetric cross-modal attention mechanism to focus on pressure signals that are sensitive to leakage. We integrate vibration and acoustic features and fuse them at the feature level, fundamentally solving the heterogeneity and spatio-temporal mismatch problems of multi-source asynchronous sensor data. Secondly, we propose an embedded adaptive spectral normalization module, which is a dynamic frequency-domain filtering technology that collaboratively optimizes with the network. It intelligently suppresses noise-sensitive frequency bands and synchronously enhances leakage features, effectively overcoming the limitations of traditional static filtering. Thirdly, we propose a contrastive learning pre-training strategy for pipeline noise. By constructing a multi-modal noise enhancement library and feature decoupling mechanism, it significantly expands the inter-class distance between leakage and normal operating condition noise, enhancing the model’s generalization ability in complex noise conditions. Experimental results demonstrate that the proposed method achieves a leakage detection accuracy of 96.8%, representing a 1.6% improvement over the current state-of-the-art method.

Adaptive electricity theft detection method based on load shape dictionary of customers

Based on a recent study on the optimal FIR stack hybrid (FSH) filtering under the mean absolute error criterion, weighted FIR median hybrid (WFMH) filters are proposed in this paper. These filters do not need any complicated design procedure, as do the optimal FSH filters, but include many FSH filters commonly used in practice. Some properties of WFMH filters are studied and the applications of WFMH filters to image recovery from impulsive or Gaussian noise are considered. >