Coherent S-band radar has recently been emerged as a promising technique for ocean surface wave and current detection. It can measure ocean surface current by estimating Doppler frequency shifts from sea surface signals. However, the conventional time averaging (TA) method neglects spatial dimension information and is unavailable under low wind speed condition. Two algorithms for ocean current inversion are proposed in this letter: the spatial-temporal averaging (STA) method and the wavenumber-frequency (WF) method. In the STA method, the TA method is extended to the spatial-temporal domain. This approach fully exploits the spatial continuity of radar signals. In the WF method, a 2-D Fast Fourier Transform (2-D FFT) is applied to transform the spatial-temporal radial velocities into the wavenumber-frequency domain. After employing dual filtering to eliminate nonlinear components, the radial current velocity is estimated through a modified dispersion relation fitting. The two methods are based on different physical mechanisms: the STA method measurements include wind drift components, while the WF method remains unaffected by wind drift. Therefore, wind drift can be effectively estimated by calculating the difference between the two methods’ measurements. Validation using observational data collected at Beishuang Island during Typhoon Catfish shows that the estimated wind drifts achieve a correlation coefficient (COR) of 0.90 with the “empirical model predictions”. This confirms the effectiveness of the proposed algorithms.

In this letter, we propose a novel methodology for retrieving wave parameters, i.e., significant wave height and mean wave period, in near-nadir looking mode using a 77 GHz frequency-modulated continuous-wave (FMCW) multipleinput– multiple-output (MIMO) radar. First, the range-Doppler spectrum is estimated from the raw radar data, and the time-Doppler spectrum in the desired direction is obtained by integrating the digital beamforming algorithm with MIMO array techniques. Next, the radial velocity series are calculated using the spectral moment method. A Fourier transform is then applied to estimate the wave height spectrum from the radial velocity series, and the significant wave height and mean wave period can be obtained by the moment estimation method. Finally, the results obtained from numerical simulations and sea surface observations demonstrate that the retrieval method can extract wave parameters with reasonable performance at small incidence angles (0∼18°).

Shipboard coherent microwave radar is an emerging tool for ocean observation, which utilizes the direct relationship between the orbital wave velocity and the wave height spectrum to retrieve wave parameters. However, the ship’s motion and broken waves would introduce extra Doppler components into the Doppler spectrum of the sea echo, and accordingly degrade the performance of wave measurements. Consequently, the Doppler components should be estimated before the inversion of ocean wave parameters. To address this problem, a Doppler frequency components estimation method, which describes the problem as an optimization problem with constraints to fit the raw Doppler spectrum, is proposed. First, the Doppler spectrum model for shipboard coherent microwave radar is parameterized and simplified. Then, an objective function and the constraints are established based on the generation mechanism of frequency components in the Doppler spectrum to make the reconstructed Doppler spectrum fit the raw Doppler spectrum. Subsequently, the particle swarm optimization (PSO) algorithm is used to find the optimal coefficient to solve the optimal solution. At last, frequency components, which are produced by ship motion, broken waves, and orbital modulation of gravity waves, are estimated. To validate the proposed method, the simulation data and the experimental data collected with a shipboard S-band radar in the South China Sea in December 2020 are analyzed. The estimated frequency components from radar data are compared with the MTi-G-measured and buoy-measured data. The results indicate that the proposed method is effective for estimating the frequency components and could improve the performance of wave measurements.

Shipboard coherent microwave radar is a rapidly emerging tool for detecting the physical characteristics of ocean waves. However, the scattering mechanism for coherent microwave radar on ship has never been established for developers to put this type of radar into extensive use. To address the problem, a model incorporated with free and broken waves for shipboard coherent microwave radar is then proposed. Six-degree-of-freedom motion and forward velocity of the ship, which can reflect the real-life motion of the ship, are derived into three-coordinate ship velocity components. Then the three-coordinate ship velocity components combined with the azimuthal angle between the radar look direction and the forward direction of the ship (ABRF) are converted into the radial velocity integrated into the shipboard coherent microwave radar model. The characteristics in the Doppler spectrum of the shipboard coherent microwave radar are analyzed and explained. By comparing the simulation with the shipboard radar-measured data, the correctness of the model is verified. The variation of radar echoes with the azimuthal angle between the radar look direction and the dominant wave, the forward motion of the ship, the azimuthal angle between the radar look direction and the forward direction of the ship, and sea states are revealed. This model could provide a theoretical basis for improving the performance of ocean wave measurements on shipboard coherent microwave radar and advancing the microwave ocean remote sensing technique.

Shipboard coherent microwave radar has been a rapidly developing tool for ocean wave measurements. However, the radial velocity estimated from echoes collected with a shipboard radar is significantly affected by the forward speed and six-degrees-of-freedom (six-DOF) motion of the ship. Accordingly, the performance of ocean wave parameter inversion using such radar degrades. To address this problem, the distribution of energy modulated by the ship motion in the wavenumber-frequency spectrum is illustrated, and wave parameters inversion method based on shipboard coherent S-band radar is proposed. An adaptive filter is designed according to the distribution of the energy components modulated by the ship motion in the wavenumber-frequency spectrum. The proposed method filters out the non-wave components and preserves the wave field components. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber-frequency spectrum to obtain the spatial-temporal radial velocities. Then the wave height spectrum is estimated from the radial velocities based on the direct relationship between the radial velocity spectrum and the wave height spectrum. Later, the significant wave height and mean wave period can be derived from the wave height spectrum. A dataset collected with a shipboard coherent S-band radar in the South China Sea in December 2020 is analyzed. The ship deployed with the radar sailed around a wave buoy according to a route plan for two days in that experiment. Comparisons between the radar-estimated and buoy-measured measurements in cases of ship motion are conducted. The results indicate that the proposed method can invert wave parameters well.

For coherent microwave radar, the Bragg scattering from the broken-short waves generated after wave breaking usually introduces extra low-frequency components in the estimated wave height spectrum and, thus, leads to inaccurate retrievals of wave parameters, especially the overestimation of wave period. In order to eliminate the impacts of wave breaking, some methods based on the removal of “group line” in the spatial–temporal domain are proposed to estimate wave parameters. However, these methods are not suitable for the case that the spatial–temporal data are not available. To address this problem, a method is proposed to invert wave parameters only from the time-Doppler spectrum. Temporal velocity series are derived from the time-Doppler spectrum in which breaking components are removed. Then, the wave height spectrum from which wave parameters can be obtained is estimated from the velocity series by the direct transform relationship based on the linear wave theory. Without spatial–temporal data, the “group line” can be removed using the proposed method, and the method is validated by simulation. In addition, an approximately 11-day dataset collected with a shore-based coherent S-band radar deployed along the coast of Zhejiang province in China is reanalyzed and used to retrieve significant wave height and mean wave period. Compared with the buoy-measured data, the significant wave heights and mean wave periods retrieved by the proposed method have the root-mean-square differences (RMSDs) of 0.25 m and 0.60 s, respectively, and also have the correlation coefficients (CCs) of 0.94 and 0.81, respectively. The results indicate that the proposed method can invert wave parameters from the time-Doppler spectrum with a reasonable performance.

SUMMARY: Numerous metagenomics projects have produced tremendous amounts of sequencing data. Aligning these sequences to reference genomes is an essential analysis in metagenomics studies. Large-scale alignment data call for intuitive and efficient visualization tool. However, current tools such as various genome browsers are highly specialized to handle intraspecies mapping results. They are not suitable for alignment data in metagenomics, which are often interspecies alignments. We have developed a web browser-based desktop application for interactively visualizing alignment data of metagenomic sequences. This viewer is easy to use on all computer systems with modern web browsers and requires no software installation. AVAILABILITY: http://weizhongli-lab.org/mgaviewer

The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.

SUMMARY: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. AVAILABILITY: http://cd-hit.org. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Fragment recruitment, a process of aligning sequencing reads to reference genomes, is a crucial step in metagenomic data analysis. The available sequence alignment programs are either slow or insufficient for recruiting metagenomic reads. We implemented an efficient algorithm, FR-HIT, for fragment recruitment. We applied FR-HIT and several other tools including BLASTN, MegaBLAST, BLAT, LAST, SSAHA2, SOAP2, BWA and BWA-SW to recruit four metagenomic datasets from different type of sequencers. On average, FR-HIT and BLASTN recruited significantly more reads than other programs, while FR-HIT is about two orders of magnitude faster than BLASTN. FR-HIT is slower than the fastest SOAP2, BWA and BWA-SW, but it recruited 1-5 times more reads. AVAILABILITY: http://weizhongli-lab.org/frhit.

MOTIVATION: Pair-wise residue-residue contacts in proteins can be predicted from both threading templates and sequence-based machine learning. However, most structure modeling approaches only use the template-based contact predictions in guiding the simulations; this is partly because the sequence-based contact predictions are usually considered to be less accurate than that by threading. With the rapid progress in sequence databases and machine-learning techniques, it is necessary to have a detailed and comprehensive assessment of the contact-prediction methods in different template conditions. RESULTS: We develop two methods for protein-contact predictions: SVM-SEQ is a sequence-based machine learning approach which trains a variety of sequence-derived features on contact maps; SVM-LOMETS collects consensus contact predictions from multiple threading templates. We test both methods on the same set of 554 proteins which are categorized into 'Easy', 'Medium', 'Hard' and 'Very Hard' targets based on the evolutionary and structural distance between templates and targets. For the Easy and Medium targets, SVM-LOMETS obviously outperforms SVM-SEQ; but for the Hard and Very Hard targets, the accuracy of the SVM-SEQ predictions is higher than that of SVM-LOMETS by 12-25%. If we combine the SVM-SEQ and SVM-LOMETS predictions together, the total number of correctly predicted contacts in the Hard proteins will increase by more than 60% (or 70% for the long-range contact with a sequence separation > or =24), compared with SVM-LOMETS alone. The advantage of SVM-SEQ is also shown in the CASP7 free modeling targets where the SVM-SEQ is around four times more accurate than SVM-LOMETS in the long-range contact prediction. These data demonstrate that the state-of-the-art sequence-based contact prediction has reached a level which may be helpful in assisting tertiary structure modeling for the targets which do not have close structure templates. The maximum yield should be obtained by the combination of both sequence- and template-based predictions.

A self-organizing and self-evolving agents (SOSENs) neural network is proposed. Each neuron of the SOSENs evolves itself with a simulated annealing (SA) algorithm. The self-evolving behavior of each neuron is a local improvement that results in speeding up the convergence. The chance of reaching the global optimum is increased because multiple SAs are run in a searching space. Optimum results obtained by the SOSENs are better in average than those obtained by a single SA. Experimental results show that the SOSENs have less temperature changes than the SA to reach the global minimum. Every neuron exhibits a self-organizing behavior, which is similar to those of the self-organizing map (SOM), particle swarm optimization (PSO), and self-organizing migrating algorithm (SOMA). At last, the computational time of parallel SOSENs can be less than the SA.

In this paper, support vector machines (SVMs) are studied in the application of transient stability assessment in power systems. SVMs have the following advantages: automatic determination of the number of hidden neurons, fast convergence rate, good generalization capability, etc. SVMs use the principle of structural risk minimization, and thus reduce the dependency of experience unlike neural networks and have better generalization and classification precision. Furthermore, SVMs are solved by the 2nd order convex programming and the final solution of SVMs is sole and optimal. The performance of SVMs depends on the type of kernel functions and the parameters of kernel functions, which are determined by experience or experiments. So the effects of kernel functions and the parameters of kernel functions are analyzed by experiments in the paper. In addition, Experiments corroborate the superiority of v-SVM applied in TSA in power systems by comparing with BP and RBE.

Self-organizing map (SOM) is an approach of nonlinear dimension reduction and can be used for visualization. It only preserves topological structures of input data on the projected output space. The interneuron distances of SOM are not preserved from input space into output space such that the visualization of SOM can be degraded. Visualization-induced SOM (ViSOM) has been proposed to overcome this problem. However, ViSOM is derived from heuristic and no cost function is assigned to it. In this paper, a probabilistic regularized SOM (PRSOM) is proposed to give a better visualization effect. It is associated with a cost function and gives a principled rule for weight-updating. The advantages of both multidimensional scaling (MDS) and SOM are incorporated in PRSOM. Like MDS, The interneuron distances of PRSOM in input space resemble those in output space, which are predefined before training. Instead of the hard assignment by ViSOM, the soft assignment by PRSOM can be further utilized to enhance the visualization effect. Experimental results demonstrate the effectiveness of the proposed PRSOM method compared with other dimension reduction methods.

A radial-basis-function (RBF) neural network based fault detection system is developed for performing induction machine fault detection and analysis. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting, grid (CSG) algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. The proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.

In this paper, a new algorithm of support vector visualization and clustering (SVVC) based on self-organizing map (SOM) and support vector one-class classification (SVOCC) is presented. Original SVOCC is to identify the support domain of input data. When it is used for clustering, the high computational complexity for identifying cluster gaps between any pair points makes it less likely to be used in large data sets. In addition, the identified clusters cannot be visually displayed in high dimensions larger than three. Self-organizing map (SOM) is a neural network approach, which can project high-dimensional data into usually 2-D grid while preserving topology of input data. By using the proposed SVVC algorithm, resulting map can visually display high-dimensional cluster shapes and corresponding clusters can be found. Outliers and cluster borders can be clearly identified on the map, which is better than other visualization and clustering methods on SOM. The computational complexity of SVVC is less than the method of directly clustering by SVOCC.