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Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional...
The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed...
It is meaningful to find an effective method to solve the problem of underwater image detail loss and contrast decrease caused by turbulence. In this work, we present a novel method to enhance edge and the contrast of the image. First, we separate the high frequency and low frequency parts of underwater image by using the HWD, and then we remove the image noise and enhance the edge of images. We also...
Using machines to save manpower has always been the core concept of technological development. For some time, the process of replacing the brain has made great progress in the development of decision-making, such as identification, and has made a wide range of applications. The self-driving car as the representative of the intelligent robot has become a hot topic. Based on the scene of the use of...
Convolutional neural networks (CNNs) excel in various computer vision related tasks but are extremely computationally intensive and power hungry to run on mobile and embedded devices. Recent pruning techniques can reduce the computation and memory requirements of CNNs, but a costly retraining step is needed to restore the classification accuracy of the pruned model. In this paper, we present evidence...
As an underwater detection sensor, side-scan sonar plays an important role in marine survey, mineral exploration, underwater archaeology and so on. During the use of side-scan sonar, classifiication and mosaicking of collected images is essential in most cases. There are two main contributions in our work. On the one hand, we propose a supervised learning method based on kernel-based extreme learning...
The challenge in the Autonomous Underwater Vehicle (AUV) navigation technology is how to ensure precise localization accurately. Extended Kalman filter (EKF) is the most widely used navigation method. Despite its long successful application, EKF has a number of problems in application, for instance, the system motion model usually not appropriate to be described as a linear system. This paper proposed...
Side-scan sonar image segmentation is an important part in marine surveys, especially when we need to get the topographic features of the seabed. Actually, due to the complexity of the geomorphological characteristics in the seabed, we can't obtain any prior knowledge. Therefore we need an unsupervised system to segment side-scan sonar images automatically. In this paper, a novel segmentation system...
Nowadays, the challenge of learning from large scale and imbalanced data set have attracted a great deal of attention from both industry and academia, which is also deemed to be an important task for fraud detection in telecommunication, finance, online commerce. In general, it's almost impossible to train a classification model on the complete data set, especially in the era of big data, due to the...
Bare bones particle swarm optimization (BBPSO) algorithm, a swarm intelligence algorithm, is famous for its easy applying and parameter-free. That is why its principles and applications have been studied by a IoT of scholars in recent years. However, quickly losing the diversity of the swarm still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm...
We propose and analyze a 3-tier cloud-cloudlet-device hierarchical trust management protocol called IoT-HiTrust for large-scale IoT systems. Our mobile cloud hierarchical trust management protocol allows an IoT device to report its service experiences and query the trustworthiness of another IoT device for service composition and selection following a simple localized report-and-query paradigm. We...
Autonomous underwater vehicles (AUVs) work in complex marine environments, and sensors play an important role in AUV systems. Therefore, research on sensor failure diagnosis technology is important for improving the reliability of AUV systems. In this paper, a new method combining phase space reconstruction and extreme learning machine (ELM) is proposed. This method is applied to predict sensor output...
As we all known, autonomous navigation system is the basic of AUV movement, besides, accurate position estimation and reliable measurement are the keys of it. In order to avoid accumulating position errors in the long voyage by using navigation algorithm, we can depend on GPS. Due to the influence of diving and floating process, GPS data would slip. The slips affect the accuracy of navigation seriously...
As an important role of oceanographic survey, side-scan sonar image classification has attracted much attention in the past two decades. Due to the special properties of sonar image, traditional approaches are difficult to get good classification accuracy, so their implementation in real world is blocked. In this paper, a novel classification system based on kernel-based extreme learning machine (KELM)...
Precise positioning of AUV plays an important role in the efficient and reliable underwater operation. The extended Kalman filter (EKF) is the most commonly used method, because this algorithm is easy to implement. However, EKF is only effective for nonlinear systems with approximate linearity, then truncation error is introduced. When the initial state error is large or the system model has high...
Autonomous Underwater Vehicles (AUVs) are important platform for oceanographic survey. AUVs have been widely applied to many fields, such as the ocean research, oil and gas exploitation, mineral resources investigation, fishing and military. People can obtain important ocean information by segmenting, classifying and recognizing sonar image of AUV. So studying side-scan sonar image is significant...
Big data analytics makes predicting human behavior possible, but it is unclear how to exploit the predictable information for improving performance of wireless networks. In this paper, we investigate the potential of predictive resource allocation in supporting high throughput by exploiting excess resources. To this end, we assume that the requests and trajectories of mobile users and the average...
In this paper, we describe our experience in writing parallel numerical algorithms using Hierarchically Tiled Arrays (HTAs). HTAs are classes of objects that encapsulate parallelism. HTAs allow the construction of single-threaded parallel programs where a master process distributes tasks to be executed by a collection of servers holding the components (tiles) of the HTAs. The tiled and recursive...
There has been a great development of antenna for synthetic aperture radar (SAR) applications in China in the past dozen years. The active phased array is applied to airborne and space-borne SAR system. As an important component of an array, a brief review on microstrip patch and waveguide slot antenna is presented in this paper mainly.
As the popularity of big data analytics has continued to grow, so has the need for accessible and scalable machine-learning implementations. In recent years, Apache Spark's machine-learning library, MLlib, has been used to fulfill this need. Though Spark outperforms Hadoop, it is not clear if it is the best performing underlying middleware to support machine learning implementations. Building on a...
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