The water detection algorithm for the dual-polarized HydroGNSS mission was validated using spaceborne left-hand circular polarization (LHCP) data from the cyclone global navigation satellite system. With the public av...
详细信息
The water detection algorithm for the dual-polarized HydroGNSS mission was validated using spaceborne left-hand circular polarization (LHCP) data from the cyclone global navigation satellite system. With the public availability of dual-polarization data from Rongowai, an airborne global navigation satellite system-reflectometry mission, a unique opportunity arises to evaluate the contribution of right-hand circular polarization (RHCP) data to surface water detection. This analysis can offer a deeper understanding of RHCP data and yield predictive insights prior to the HydroGNSS launch. In this study, we initially analyzed coherence indicators in incoherently averaged dual-polarized signals, and subsequently, applied these indicators to a random forest classifier, similar to the HydroGNSS surface inundation algorithm. The findings have been compared with existing flooding products, showing promising results with over 91% agreement in water detection. The analysis revealed that, while the LHCP data exhibit a higher sensitivity to water, the incorporation of the RHCP data enhances the robustness and reliability of the classification. This reinforces the hypothesis that HydroGNSS, operating at dual polarization, might produce a more effective surface water detection product than single-polarization GNSS-R missions.
In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (D...
详细信息
In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used methods. Each has benefits and limitations, suited to different data types. However, EWMA and CUSUM are fixed mean drift detectors, limiting their applicability and adaptability. This article explores adding dynamic behavior to drift detection methods. We use a wide range of synthetic data based on a real-world manufacturing process. The investigated parameter space includes standard deviation, drift rates, and outliers. Besides, each algorithm has some tuning parameters that define its behavior. Two metrics validate experiments against labeled data. Based on our observations, EWMA performs better for drift detection on average, but CUSUM is superior in detecting very small drifts. Furthermore, we derive guidelines for the choice and application of drift detection in practice.
Automatic heart sound detection plays a vital role in the early detection of cardiovascular diseases (CVDs). In recent years, many heart sound detection algorithms have been proposed, becoming a popular research topic...
详细信息
Automatic heart sound detection plays a vital role in the early detection of cardiovascular diseases (CVDs). In recent years, many heart sound detection algorithms have been proposed, becoming a popular research topic in medical diagnosis. In this article, our goal is to answer which heart sound detection algorithms and heart sound features perform best in which situations and to explore the problems in existing research. This work will provide ideas for subsequent research in this area. We achieve this aim through a standard process that includes preprocessing, segmentation, feature extraction, and classification. We discuss the length and overlap rate in segmentation and analyze the classification methods, especially the nine most salient heart sound features. The performance of the existing techniques is evaluated in different datasets and at the same level of comparison. The experiments show that the segmentation length includes at least one complete period, and we should use a large overlap rate in the segmentation phase. For feature extraction, time-domain feature (TIME) and fast Fourier transform (FFT) features based on single independent variables perform better in deep models and traditional classifiers. Short-time Fourier transform (STFT), continuous wavelet transform (CWT), and S-transform (ST) features based on double independent variables can perform well in all classification models;Mel spectrum/Mel frequency cepstrum coefficient (MFCC) is only better on deep neural networks (DNNs). The best performance is achieved by combining TIME or MFCC with DNNs.
The performance of mode division multiplexing (MDM) systems is limited by mode-dependent loss (MDL), which seriously deteriorates the unitarity of the transmission matrix, making the traditional detection algorithms i...
详细信息
The performance of mode division multiplexing (MDM) systems is limited by mode-dependent loss (MDL), which seriously deteriorates the unitarity of the transmission matrix, making the traditional detection algorithms ineffective. In order to mitigate the effect of MDL in MDM transmission and improve the efficiency of digital signal processing at the receiver, a novel coordinate descent (CD)-based box-constrained detection algorithm using adaptive moment estimate (CDbox-Adam) is proposed. In the process of each coordinate update for the CDbox detection algorithm, the adaptive moment estimation (Adam) method adaptively updates the step size for different parameters from the first moment estimate and the second moment estimate of the gradients. To simplify the process of each coordinate updating of the CDbox-Adam algorithm, the CDbox-Adamax detection algorithm is also proposed. The CDbox-Adamax detection algorithm generalizes the 2-norm update rule of weights to the infinite norm. At the same time, an early stop criterion is proposed to efficiently avoid unnecessary iterations. Simulation results show that the early stop criterion reduces the number of iterations of the proposed detection algorithms. For a 12-mode MDM system impaired by MDL, the average number of iterations of the proposed detection algorithms is less than the CDbox detection algorithm. Thus, the proposed detection algorithms present lower computational complexity than the CDbox detection algorithm. Furthermore, compared with the conventional minimum mean square error (MMSE) detection algorithm, the proposed detection algorithms, CDbox-Adam and CDbox-Adamax, gain signal to noise ratio (SNR) improvement of 2.3 dB and 1.6 dB at BER = 10(-5) when MDL = 5 dB with QPSK and 16QAM, respectively. In the presence of MDL = 5 dB and 10 dB, when compared with the proposed detection algorithms with perfect CSI, the proposed detection algorithms with LS channel estimation requires 5.1 dB and 2.5 dB more of SNR to get
The evaluation of a change detection algorithm should show its superiority over state-of-the-art algorithms' performances. Evaluating an algorithm involves executing it to segment a set of videos and comparing the...
详细信息
The evaluation of a change detection algorithm should show its superiority over state-of-the-art algorithms' performances. Evaluating an algorithm involves executing it to segment a set of videos and comparing the results with the ground truth. Here, we used the difficulty level to classify each pixel of each frame of the videos of a dataset as an algorithm performance measure. A structure called "difficulty map" stores information about the difficulty of classifying each pixel in a frame. Based on these maps, we developed a metric that aims to evaluate the performance of algorithms on the difficulty map. The results showed that there are algorithms with the characteristic of classifying pixels that most state-of-the-art algorithms cannot classify (promising algorithms). Identifying such algorithms is essential since improving their performance means facing challenges already overcome by existing approaches.
A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that re...
详细信息
A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.
With increased levels of digitalization and networked systems, maintaining the security of water utilities has gained prime importance. Digital infrastructure has tremendously improved operational efficiency for the w...
详细信息
ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
With increased levels of digitalization and networked systems, maintaining the security of water utilities has gained prime importance. Digital infrastructure has tremendously improved operational efficiency for the water system but exposed its critical systems to various cyber threats. The cybersecurity of water utilities would alleviate the risks of water supply contamination, service disruption, and economic consequences. detection algorithm evaluation is thus a crucial step in strengthening security measurements. The Battle of the Attack detection algorithms BATADAL gives an exclusive opportunity to rigorously test the strength of several detection algorithms designed to apply to water systems. Realistic cyber-attacks simulated through BATADAL will go a long way in rigorous testing and evaluation so researchers and practitioners can pinpoint the most potent detection strategies. A comparative evaluation for the precision of Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) shows that SVM is superior to RF and DT because of its robustness in dealing with high-dimensional data. Precision is high since the decision boundary is optimized for SVM, so the best performance is in SVM among the three, and the observed accuracy is 97.09. This enhances the water infrastructure's ability and safeguards vital resources while ensuring the community's safe water supply is not interrupted.
In the field of network analysis, methods for identifying community structure often involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. However, i...
详细信息
ISBN:
(数字)9798331533816
ISBN:
(纸本)9798331533823
In the field of network analysis, methods for identifying community structure often involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. However, in practice, we often encounter multiple division schemes with high quality scores that are close to the overall optimum. In fact, an accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying on a single optimal solution alone. However, such a collection of network divisions may be difficult to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, this paper introduces an innovative strategy to reveal the diversity of network community structures more comprehensively and to provide a set of more globally-visioned delineation results by clustering similar network divisions and then selecting representative divisions from each cluster.
An emerging field of study called autonomous driving seeks to create self-driving cars that can guarantee the safety of both passengers and other road users. Although several countries have already developed basic aut...
详细信息
ISBN:
(数字)9798350349221
ISBN:
(纸本)9798350349238
An emerging field of study called autonomous driving seeks to create self-driving cars that can guarantee the safety of both passengers and other road users. Although several countries have already developed basic autonomous vehicles, more research is required for more sophisticated automation. Numerous different computer vision (CV) algorithms are needed to handle the extensive elements in an autonomous vehicle. Although many computer vision algorithms have already been built as software, they still require a stable hardware platform in order to be tested and used. Due to the many advantages it offers, Field Programmable Gate Array (FPGA) has become the most popular hardware platform. This paper addresses the significance and necessity of autonomous vehicles, highlighting one of their most difficult features— pedestrian detection. The paper also reviews the numerous advantages of FPGAs specifically for the development of autonomous driving challenging features like pedestrian detection and it also documents that how different researchers have used FPGA’s as their hardware platform to construct self-driving car’s features with an eye towards potential future improvements.
In scenarios of autonomous or semi-autonomous Vertical Takeoff and Landing based Un-manned Areal Vehicle deployment, the capacity to swiftly and accurately identify a viable Safe Landing Zone is paramount for the oper...
详细信息
ISBN:
(数字)9798350349610
ISBN:
(纸本)9798350349627
In scenarios of autonomous or semi-autonomous Vertical Takeoff and Landing based Un-manned Areal Vehicle deployment, the capacity to swiftly and accurately identify a viable Safe Landing Zone is paramount for the operational safety and efficacy of UAV missions. A Safe Landing Zone detection (SLZD) algorithm takes real-time perception data as input and outputs all the potential Safe Landing Zones that meet the criteria set by the hazard metrics. SLZD algorithms categorized based on their input data types are briefly discussed. Reasoning for choice of selecting LiDAR Point-Cloud based Safe Landing Zone detection (PC-SLZD) algorithms for the scope of this research paper is provided. The general structure of such PC-SLZ algorithm pipeline is provided. Sub-algorithms used to implement the components of this pipeline are listed. Potential main algorithms that are constructed by combining all promising sub-algorithms are proposed for further evaluation. The goal of this research paper is to provide an overview of PC-SLZD algorithms and propose a three-stage evaluation procedure. This procedure addresses four key requirements: robustness, accuracy, precision and computational efficiency, to facilitate selection of promising candidates in given applications.
暂无评论