DYNAMO: An adaptive spatio-Temporal attention method for provenance graph-based APT detection

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.

In this paper, an adaptive edge detection method (Canny operator and Otsu threshold selection based adaptive edge detection method - COAED) is proposed. The COAED method combines a new hybrid filter with Canny operator to avoid the conflict of Canny operator between noise removing and edge locating, and uses Otsu threshold selection method to determine Dual-threshold of Canny operator adaptively. The new hybrid filter firstly judges whether the pixel is polluted by impulse noise, and then uses a corresponding filter to process the current pixel. A median filter is used if the pixel is thought to be impulse noise; otherwise an improved mean filter is selected to weaken the Gaussian noise. After the image is smoothed by the hybrid filter, a Canny operator with small Gaussian variance is used to extract edge. Because only part of Gaussian noise remains, Canny operator with small Gaussian variance can suppress the noise and preserve the edge effectively. Using the gauge image polluted by hybrid noise as experiment object, the performance of COAED method is evaluated qualitatively and quantitatively. Experimental results show that the COAED method is superior to Canny operator.

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.

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.

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

Lane detection and tracking technique is the autonomous driving basis for Rubber-Tired Gantries (RTGs), vital to the automation and intelligence updating of man-driven container terminals. However, the existing lane detection methods developed for common road scenarios cannot meet the high-precision and robust all-weather requirements of RTG autonomous driving. In this article, we propose an Adaptive Edge-based Lane Detection and Tracking method considering RTG lanes’ characteristics in this paper. First, the candidate edges of lane lines are detected and paired based on the enhanced gradient features. Next, inverse perspective mapping is employed to search the right edges, followed by an adaptive sliding-window method. Ultimately, we develop an adaptive Kalman filter to track lane lines robustly, detecting confidence weighting by relaxing the constraint of lane line width. The proposed method is tested in an actual container yard, the lane centerline’s average position error is 2.051 pixels, and the detection success rate is close to 100%.

Adaptive online event detection in news streams

In this paper, we present an adaptive multi-feature matching method for target detection and target tracking based on an occupancy grid map in dynamic outdoor environments on a moving vehicle equipped with laser radar. The raw scanning points are divided into blocks and the matching strategy of ICP (Iterated Closest Points) algorithm is modified by using the multi-feature information of objects. We introduce a method to adaptively correct the weighting coefficients of the multi-feature in the similarity function. After matching the objects in the two adjacent frames, the online grid map is updated by Bayesian theory and the probability of inverse sensor model. The moving objects are detected with grid map matching. Then, the moving targets are finally tracked by tracker management coupled with Kalman filter. In the experiment, the online grid map is established and the stable results for targets tracking are also achieved.

This paper presents an innovative method for skin detection under changing lighting conditions. The proposed algorithm employs a Gaussian skin color model to classify skin pixels. This Gaussian model is adapted beforehand to accommodate the current lighting condition. AT first, the color temperature of the light source is estimated from a skin sample. Then the skin sample is going through a color constancy process to produce a corrected skin sample. The means of the raw and corrected skin samples are utilized to transform the Gaussian model. Experiments, using different adaptive skin detection methods and the proposed algorithm, have been conducted. Test results assert the effectiveness of the proposed method for skin detection under changing lighting conditions.