In this paper, we propose a dynamic trajectory-based adaptive scheduling medium access control (DTAS-MAC) protocol, which aims to illustrate where the AUV should hover to receive data and how the AUV should interact with sensor nodes in AUV-assisted underwater data collection. While cruising in the sea, the AUV wakes up sensor nodes with data transmission demands and obtains their real-time status information through message exchanges. Then, the AUV dynamically determines a hovering point to receive data from the sensor nodes with the goal of maximizing data collection efficiency. Movement energy consumption, hovering energy consumption, and the number of error-free packets received by the AUV are considered in the data collection efficiency model. To reduce packet collisions and improve channel utilization, a scheduling-based mechanism is proposed to organize data transmission from sensor nodes. When scheduling sensor nodes to send data to the AUV, the transmission priority is assigned based on their upload urgency. The experimental results verify the effectiveness of the DTAS-MAC protocol in improving data collection efficiency and throughput, while reducing data loss.

Underwater acoustic (UWA) communication is until now the only effective means for long distance underwater wireless communication, and hence it is the key foundation for Internet of Underwater Things (IoUT). However, in ocean environment, impulsive noise (IN) generated by natural and human factors usually seriously affects the performance of UWA communication. In this article, utilizing the powerful capability of deep learning, a 1-D convolutional neural network based on multiattention mechanism (1DCNN-MAM) for IN mitigation in UWA orthogonal frequency division multiplexing (OFDM) systems is proposed. To enhance the generalization performance of the network, it utilizes minimization of the energy on null subcarriers as an auxiliary task for network training. Furthermore, to adapt to specific environment quickly and reduce the amount of real data required for training, it adopts a network-based deep transfer learning approach for fine-tuning. To verify the performance of the proposed scheme, a sea trial has been carried out along with simulations, and both demonstrate that the proposed scheme can effectively suppress the IN in UWA OFDM systems.

The variation of Link quality in underwater sensor networks often lead to link interruption and void-hole. To address the above issues, some protocols chose nodes with good link quality as relay nodes without considering fairness, while others used multipath and greedy forwarding which cause high energy consumption. Therefore, this article proposes a fair and energy efficient cooperative routing protocol based on link quality for underwater acoustic sensor network (FECRP). This protocol divides underwater sensor networks into clusters, selects cluster heads and relay nodes based on perceived link quality, it cooperatives with MAC protocol in the data transmission within the cluster, scheduling the repeated transmission of data packets to overcome link interruptions. The simulation results show that this protocol improves fairness and reduces energy consumption while ensuring throughput.

Due to its relatively lower propagation loss, acoustic wave is until now the only option for medium and long range underwater wireless communication. However, the low velocity of acoustic wave in water can easily lead to significant Doppler effect. Especially in the case of communication between underwater platforms with high mobility, the Doppler effect could even be time varying during signal transmission. In this paper, an iterative Doppler tracking algorithm based on Kalman filter is proposed for non-coherent frequency hopping frequency shift keying underwater acoustic communication (UWA) systems suffered from varying Doppler effect. Since the duration of a UWA communication signal is usually short, the change in relative motion between transmitter and receiver is not too drastic.Therefore the motion during this period is modeled by constant acceleration model or constant jerk model. Then, a receiver algorithm iteratively estimates and refines the time-varying Doppler is proposed, in which the refinement is carried out based on Kalman filter. Utilizing the filtered Doppler scale factors, this algorithm adaptively performs symbol synchronization and Doppler frequency shift correction in a symbol-by-symbol fashion, which effectively compensates the signal distortion induced by varying Doppler effect. Simulation results demonstrate the effectiveness of the proposed algorithm in different maneuvering scenes.

Image deraining aims to improve the visibility of images damaged by rainy conditions, targeting the removal of degradation elements such as rain streaks, raindrops, and rain accumulation. While numerous single image deraining methods have shown promising results in image enhancement within the spatial domain, real-world rain degradation often causes uneven damage across an image's entire frequency spectrum, posing challenges for these methods in enhancing different frequency components. In this paper, we introduce a novel end-to-end Adaptive Frequency Enhancement Network (AFENet) specifically for single image deraining that adaptively enhances images across various frequencies. We employ convolutions of different scales to adaptively decompose image frequency bands, introduce a feature enhancement module to boost the features of different frequency components and present a novel interaction module for interchanging and merging information from various frequency branches. Simultaneously, we propose a feature aggregation module that efficiently and adaptively fuses features from different frequency bands, facilitating enhancements across the entire frequency spectrum. This approach empowers the deraining network to eliminate diverse and complex rainy patterns and to reconstruct image details accurately. Extensive experiments on both real and synthetic scenes demonstrate that our method not only achieves visually appealing enhancement results but also surpasses existing methods in performance. The source code is available at https://github.com/yanfefei/AFENet.

Optical remote sensing serves as a critical technology for exploring underwater environments. However, light absorption and scattering underwater significantly degrade underwater optical images, affecting the extraction and analysis of information. Underwater image enhancement (UIE) methods aim to eliminate this degradation and improve the visual quality of images. Nonetheless, the complex and dynamic underwater imaging environment, limited computing resources, and scarce training data/data pairs restrict the practical application of existing methods. To solve these problems, we propose an UIE network (UIEN) based on neuromorphic computing, which simulates the pathway of the visual system to perceive and process light information, and can use a lightweight network structure to achieve good performance through unsupervised learning. Specifically, we propose a visual perception module comprising a 2-D Duffing oscillator (2D-DO) with pixel-wise potential barrier parameters. This module can generate the stochastic resonance (SR) phenomenon to enhance the degraded image. Inspired by physics-informed learning, a dual-path neural network is employed to estimate the potential barrier parameters and solve the partial differential equation (PDE) that describes the visual perception module. Subsequently, we introduce three nonreference (NR) losses to guide the network training and improve the enhanced image’s visual quality. Extensive experiments demonstrate that the proposed method can achieve outstanding performance with less computing resource cost compared to state-of-the-art (SOTA) methods. Furthermore, we examine the generalization and versatility of the proposed method to establish its reliability across various degradation types and tasks in practical applications of optical remote sensing.

In this paper, we propose a novel target detection algorithm that addresses the challenge of difficult recognition and localization in sea surface general purpose target detection.The proposed algorithm is based on an improved YOLOv7, incorporating an efficient non-parametric attention mechanism module-SimAM into the original network, which reduces the model parameters and enhances the expressiveness of the network as well as the extraction ability of the model for important features.Additionally, we introduce a new module, CN-CSP, that merges the strengths of CSP and ConvNext, thereby improving the network's learning ability while reducing the computational overhead.Furthermore, the integration of the rssp module into the backbone of YOLOv7 enables the network to extract features in a more comprehensive and multi-scale manner.Experimental results on The Sea Surface Target Dataset indicate the superiority of the proposed algorithm, achieving detection accuracy of 78.3% with improvements of 3.1% compared to the original YOLOv7 model.

Underwater object segmentation is one of the key technologies in the fields of marine biology research and autonomous underwater vehicles. The challenges of underwater object segmentation originate from two aspects, 1) the complex underwater environment and 2) the camouflage characteristics of marine animals. In this paper, we propose WaterSNet, an underwater object segmentation network to address these challenges. Specified, we propose a random style adaption (RSA) module as well as a siamese structure to reduce the impact of water degradation diversity. We also extract multi-scale features via the receptive field block (RFB) module, and then fuses multi-level features to better utilize global context information via the attention fusion block (AFB) module. Experimental results on marine animal dataset MAS3K demonstrate that the proposed method outperforms other state-of-the-art methods significantly. The code will be available at: https://github.com/ruizhechen/WaterSNet/

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

In sonar applications, important information such as distributions of minerals, underwater creatures has a high probability of being contained in sonar images. In many underwater applications such as underwater rescue and biometric tracking, it is necessary to send sonar images underwater for further analysis. Due to the bad conditions of underwater acoustic channel and current underwater acoustic communication technologies, sonar images very possibly suffer from several typical types of distortions. As far as we know, limited efforts have been made to gather meaningful sonar image databases and benchmark reliable objective quality model, so far. This paper develops a new objective sonar image quality predictor (SIQP), whose core is the combination of two features specific to a quality measure of sonar images. These two features, which come from statistical and structural information inspired by the characteristics of sonar images and the human visual system, reflect image quality from the global and detailed aspects. The performance comparison of the proposed metric with popular and prevailing quality evaluation models is conducted using a newly established sonar image quality database. The results of experiments show the superiority of our SIQP metric over the available quality evaluation models.

Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Sonar imagery plays a significant role in oceanic applications since there is little natural light underwater, and light is irrelevant to sonar imaging. Sonar images are very likely to be affected by various distortions during the process of transmission via the underwater acoustic channel for further analysis. At the receiving end, the reference image is unavailable due to the complex and changing underwater environment and our unfamiliarity with it. To the best of our knowledge, one of the important usages of sonar images is target recognition on the basis of contour information. The contour degradation degree for a sonar image is relevant to the distortions contained in it. To this end, we developed a new no-reference contour degradation measurement for perceiving the quality of sonar images. The sparsities of a series of transform coefficient matrices, which are descriptive of contour information, are first extracted as features from the frequency and spatial domains. The contour degradation degree for a sonar image is then measured by calculating the ratios of extracted features before and after filtering this sonar image. Finally, a bootstrap aggregating (bagging)-based support vector regression module is learned to capture the relationship between the contour degradation degree and the sonar image quality. The results of experiments validate that the proposed metric is competitive with the state-of-the-art reference-based quality metrics and outperforms the latest reference-free competitors.

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information (CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.

One of the most critical missions of sonar is to capture deep-sea pictures to depict sea floor and various objects, and provide an immense understanding of biology and geology in deep sea. Due to the poor condition of underwater acoustic channel, the captured sonar images very possibly suffer from several typical types of distortions before finally reaching to users. Unfortunately, very limited efforts have been devoted to collecting meaningful sonar image databases and benchmark reliable objective quality predictors. In this paper, we first generate a sonar image quality database (SIQD), including 840 images. All distorted images were collected without artificially introducing any distortions beyond those occurring during compression and transmission. The subjective quality assessment was conducted for gathering mean opinion score (MOS) to represent the image quality and existence of target (EOT) which describes whether the image is useful. Based on the built SIQD database, state-of-the-art general image quality metrics were found to poorly correlate with “ground-truth” MOS. As a consequence, this paper further develops a novel full-reference local entropy backed sonar image quality predictor (LESQP). The experimental results demonstrate the superiority of our LESQP metric over the available quality measures.

Advanced modulation and channel equalization techniques are essential for improving the performance of medium-range single-carrier underwater acoustic communications. In this paper, an enhanced detection scheme, hybrid time-frequency domain decision feedback equalizer (DFE) combined with complementary code keying (CCK) remodulator, is presented. CCK modulation technique provides strong tolerance to intersymbol interference caused by multipath propagation in underwater acoustic channels. The conventional hybrid DFE, using a frequency domain feedforward filter and a time domain feedback filter, provides good performance along with low computational complexity. The error propagation in the feedback filter, caused by feedbacking wrong decisions prior to CCK demodulation, may lead to great performance degradation. In our proposed scheme, with the help of CCK coding gain, more accurate remodulated CCK chips can be used as feedback. The proposed detection scheme is tested by the practical ocean experiments. The experimental results show that the proposed detection scheme ensures robust communications over 10-kilometre underwater acoustic channels with the data rate at 5 Kbits/s in 3 kHz of channel bandwidth.

Jamming attacks that can further lead to denial of service attacks have thrown serious threats to underwater sensor networks (UWSNs). However, due to the narrow bandwidth of underwater acoustic signals and time variant propagation environments, jamming in UWSNs cannot be fully addressed by spread spectrum techniques, one type of widely-used antijamming methods in wireless networks for decades. In this work, we investigate jamming attacks in underwater sensor networks. More specifically, the interactions between the underwater sensors and jammers in UWSNs are formulated as an underwater jamming game, in which the players choose their transmit power levels to maximize their individual utilities based on the signal to interference plus noise ratio of the legal signals and transmission costs. The Nash equilibrium (NE) of a static jamming game is presented in a closed-form expression for the jamming scenario with known acoustic channel gains. For the dynamic and unknown underwater environments, we propose a reinforcement learning-based anti-jamming method for UWSNs, in which each sensor chooses its transmit power without knowing the channel gain of the jammers. Simulations are performed to evaluate the NE in the static jamming game in underwater sensor networks and to validate the efficacy of the proposed anti-jamming power control scheme against jamming in dynamic environments.

A pedigree is a diagram of family relationships, and it is often used to determine the mode of inheritance (dominant, recessive, etc.) of genetic diseases. Along with rapidly growing knowledge of genetics and accumulation of genealogy information, pedigree data is becoming increasingly important. In large pedigree graphs, path-based methods for efficiently computing genealogical measurements, such as inbreeding and kinship coefficients of individuals, depend on efficient identification and processing of paths. In this paper, we propose a new compact path encoding scheme on large pedigrees, accompanied by an efficient algorithm for identifying paths. We demonstrate the utilization of our proposed method by applying it to the inbreeding coefficient computation. We present time and space complexity analysis, and also manifest the efficiency of our method for evaluating inbreeding coefficients as compared to previous methods by experimental results using pedigree graphs with real and synthetic data. Both theoretical and experimental results demonstrate that our method is more scalable and efficient than previous methods in terms of time and space requirements.

In this paper, the blind and semiblind channel estimation and equalization are investigated for zero-padding single-carrier block transmission (ZP-SCBT) systems using second-order statistics. Unlike the conventional channel estimation techniques, the proposed approach focuses on identifying the inverse of the channel impulse response rather than the channel response itself by skillfully exploiting the redundancy of the zero-padding. One interesting advantage of the proposed blind and semiblind approach is that the number of received blocks needed for blind identification is significantly reduced compared to the subspace method. Moreover, the computational complexity of the proposed approaches is much lower than the existing subspace methods. Simulation results are provided to demonstrate that the performance of the proposed approach is superior to the subspace-based blind methods.

Channels with a sparse impulse response arise in a variety of wireless communication applications, such as high definition television (HDTV) terrestrial transmission and underwater acoustic communications. By exploiting the sparsity of the channel, this paper proposes a fast algorithm for sparse channel estimation based on a greedy algorithm called orthogonal matching pursuit (OMP). The proposed fast OMP-based channel estimation algorithm has a low computational complexity of O (K N log N) with K and N the channel sparsity level and signal length, respectively. The fast OMP is competitive to the ℓ1-minimization based methods in terms of estimation accuracy. In addition, the fast OMP is faster and easier to implement. Therefore it is an attractive alternative to the ℓ1-minimization approaches. Simulation results are provided to demonstrate the performance of the fast OMP algorithm.

A low complexity algorithm is proposed for estimating the multipath time-of-arrival (TOA) and attenuation factors from a noisy received signal consisting of multiple overlapped echoes. Different from the conventional projection onto convex sets (POCS) method, the proposed approach harnesses the sparse property of multipath channel and the ℓ1-norm is adopted as the measurement of sparsity. The TOA estimation problem is solved by a series of projection onto convex sets, including hypersphere and hyper-polyhedra. The computational complexity of the modified POCS algorithm is O(N log N) per iteration with N being the length of the received signal. Simulation results confirm that the proposed approach provides better performance in terms of temporal resolution and robustness to noise compared with the matched filtering and the conventional POCS method.

This paper presents a feature-complete translation from SPARQL, the proposed standard for RDF querying, into efficient SQL. We propose "SQL model"-based algorithms that implement each SPARQL algebra operator via SQL query augmentation, and generate a flat SQL statement for efficient processing by relational database query engines. SPARQL-to-SQL translation presented is feature-complete, since it applies to all SPARQL language features. Finally, we demonstrate the performance and scalability of our method by an extensive evaluation using recent SPARQL benchmark queries, and a benchmark dataset, as well as a real-world photo dataset.

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Although relevance feedback (RF) has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF because of scalability, efficiency, and effectiveness issues. In this paper, we propose a unified relevance feedback framework for Web image retrieval. Our framework shows advantage over traditional RF mechanisms in the following three aspects. First, during the RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Second, the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Thus, we could integrate RF into Web image retrieval in a practical way. Last, a new user interface (UI) is proposed to support implicit RF. On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users' click-through data as implicit relevance feedback in order to release burden from the users. On the other hand, unlike traditional RF UI which hardily substitutes subsequent results for previous ones, a recommendation scheme is used to help the users better understand the feedback process and to mitigate the possible waiting caused by RF. Experimental results on a database consisting of nearly three million Web images show that the proposed framework is wieldy, scalable, and effective.

MOTIVATION: As the blueprints of cellular actions, biological pathways characterize the roles of genomic entities in various cellular mechanisms, and as such, their availability, manipulation and queriability over the web is important to facilitate ongoing biological research. RESULTS: In this article, we present the new features of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of genetic, molecular, biochemical and organismal detail. The new features include: (i) a web-based system with a new architecture, containing a server-side and a client-side, and promoting scalability, and flexible and easy adaptation of different pathway databases, (ii) an interactive client-side visualization tool for metabolic pathways, with powerful visualization capabilities, and with integrated gene and organism viewers, (iii) two distinct querying capabilities: an advanced querying interface for computer savvy users, and built-in queries for ease of use, that can be issued directly from pathway visualizations and (iv) a pathway functionality analysis tool. PathCase is now available for three different datasets, namely, KEGG pathways data, sample pathways from the literature and BioCyc pathways for humans. AVAILABILITY: Available online at http://nashua.case.edu/pathways

Although relevance feedback (RF) has been extensively studied in the information retrieval community, no commercial Web image search engines support RF because of usability, scalability, and efficiency issues. In this paper, we proposed a search result clustering (SRC) -based RF mechanism for Web image retrieval. The proposed SRC-based RF mechanism employs an effective Search Result Clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Given the textual space, we could integrate RF into Web image retrieval in a practical way. The proposed mechanism shows advantage over traditional relevance feedback methods in the following two aspects. On the one hand, our relevance feedback scheme could catch and reflect user's search intension precisely, for the noisy terms would be exempted from the term list with the aid of clustering, thus, the usability of RF in textual space for Web image retrieval is guaranteed. On the other hand, with the exemption of noisy term, the computation with regards to the low-dimensioned textual space is feasible; therefore, the issues of scalability and efficiency for Web image retrieval are addressed. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.

Although relevance feedback has been extensively studied in content-based image retrieval in the academic area, no commercial Web image search engine has employed the idea. There are several obstacles for Web image search engines in applying relevance feedback. To overcome these obstacles, we proposed an efficient implicit relevance feedback mechanism. The proposed mechanism shows advantage over traditional relevance feedback methods in the following three aspects. Firstly, instead of enforcing the users to make explicit judgment on the results, our method regards user's click-through data as implicit relevance feedback which release burden from users. Secondly, a hierarchical image search results clustering algorithm is proposed to semantically organize the search results. Using the clustering results as features, our relevance feedback scheme could catch and reflect users' search intention precisely. Lastly, unlike traditional relevance feedback user interface which hardily substitutes subsequent results for previous ones, our method employed friendly recommendation rather than substitution to let the user narrow down on the refined images. To evaluate the implicit relevance feedback mechanism, comprehensive user studies were performed

Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.