GROWTH ESTIMATES FOR SINE-TYPE-FUNCTIONS AND APPLICATIONS TO RIESZ BASES OF EXPONENTIALS

We present explicit estimates for the growth of sine-type-functions as well as for the derivatives at their zero sets, thus obtaining explicit constants in a result of Levin. The estimates are then used to derive explicit lower bounds for exponential Riesz bases, as they arise in Avdonin's Theorem on 1/4 in the mean or in a Theorem, of Bogmtr, Horvath, Job and Seip. An application is discussed, where knowledge of explicit lower bounds of exponential Riese bases is desirable.

A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes

Background： Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species. Methods： We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning （ALS） data and used Most Similar Neighbor （MSN） and Seemingly Unrelated Regression （SUR） as examples of non-parametric and parametric prediction methods, respectively Results： The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species. Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4 % and 12.6-28.9 % based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes. Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR. Conclusions： According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes. Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Source Range Estimation Based on Pulse Waveform Matching in a Slope Environment

An approach of source range estimation in an ocean environment with sloping bottom is presented. The approach is based on pulse waveform correlation matching between the received and simulated signals. An acoustic prop- agation experiment is carried out in a slope environment. The pulse signal is received by the vertical line array, and the depth structure can be obtained. For the experimental data, the depth structures of pulse waveforms are different, which depends on the source range. For a source with unknown range, the depth structure of pulse waveform can be first obtained from the experimental data. Next, the depth structures of pulse waveforms in dif- ferent ranges are numerically calculated. After the process of correlating the experimental and simulated signals, the range corresponding to the maximum value of the correlation coefficient is the estimated source range. For the explosive sources in the experiment with two depths, the mean relative errors of range estimation are both less than 7%.

Benchmark Dose Estimation for Cadmium-Induced Renal Effects Based on a Large Sample Population from Five Chinese Provinces

A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D-glucosaminidase （UNAG） was chosen as an effect biomarker. The UCd BMDLs for UNAG ranged from 2.18μg/g creatinine （cr） to 4.26μg/g cr in the populations of different provinces. The selection of the sample population and area affect the evaluation of the BMDL. The reference level of UCd for renal effects was further evaluated based on the data of all 6103 subjects. With benchmark responses （BMR） of 10%/5%, the overall UCd BMDLs for males in the total population were 3.73/2.08 μg/g cr. The BMD was slightly lower in females, thereby indicating that females may be relatively more sensitive to Cd exposure than are males.

Fault Location Approach to Distribution Networks Based on Custom State Estimator

This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.

High-resolution azimuth estimation algorithm based on data fusion method for the vector hydrophone vertical array

To aim at the problem that the horizontal directivity index of the vector hy- drophone vertical array is not higher than that of a vector hydrophone, the high-resolution azimuth estimation algorithm based on the data fusion method was presented. The proposed algorithnl first employs MUSIC algorithm to estimate the azimuth of each divided sub-band signal, and then the estimated azimuths of multiple hydrophones are processed by using the data fusion technique. The high-resolution estimated result is achieved finally by adopting the weighted histogram statistics method. The results of the simulation and sea trials indicated that the proposed algorithm has better azimuth estimation performance than MUSIC algorithm of a single vector hydrophone and the data fusion technique based on the acoustic energy flux method. The better performance is reflected in the aspects of the estimation precision, the probability of correct estimation, the capability to distinguish multi-objects and the inhibition of the noise sub-bands.

Dynamic State Estimation Based Protection of Mutually Coupled Transmission Lines

Mutually coupled lines create challenges for legacy protection schemes.In this paper,a dynamic state estimation based protection(EBP)method is proposed to address these challenges.The method requires GPS synchronized measurements at both ends of the line and a high fidelity model of the protected line.The paper presents the dynamic model of the protected line and its impact on the performance of the protection scheme.Numerical simulations prove that the method can correctly identify faults,independent of position and type.The work also demonstrates the advantages of the proposed method versus legacy protection functions such as distance protection and line differential.These advantages include reliable and faster detection of internal low impedance faults,inter-circuit faults,and high impedance faults,even in cases of 1)partially coupled lines and 2)lack of measurements in adjacent lines.

Frame synchronization for underwater acoustic communication based on maximum likelihood estimation

Traditional frame synchronization methods for underwater acoustic communication(UWAC) merely depend on correlation coefficient when synchronization signal detection is concerned and,hence,false triggering and missed synchronization can hardly be avoided in complex UWAC channels.In order to solve this problem,firstly,we analyze the effects of interference from noise,multipath and Doppler on frame synchronization;then we propose a new frame synchronization scheme based on parameter estimation.By exploiting the parameter estimation technique,we detect the synchronization signal according to the estimated parameters,thus the false triggering rate and missed synchronization rate can be reduced.We also simplify the maximum likelihood estimation to reduce computational cost.Simulation results indicate that this new scheme outperforms the traditional method in terms of delay resolution and correlation coefficient.Both static and mobile communication experimental results show that the correlation coefficient of the new scheme is higher than that of the traditional one.Moreover,the detection ability of the receiver is improved,which helps to avoid false triggering and missed synchronization.

Synthetic aperture sonar movement compensation algorithm based on time-delay and phase estimation

The effects of movement errors on imaging results of synthetic aperture sonar and the necessity of movement compensation are discussed. Based on analyzing so-called displaced phase center algorithm, an improved algorithm is proposed. In this method, the time delay is estimated firstly, then the phase is estimated for the residual error, so that the range of movement error suited to the algorithm is extended to some extent. Some simulation results on computer and experimental results in the test tank using the proposed algorithm are given as well.

On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning

The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.

Passive tracking and size estimation of volume target based on acoustic vector intensity

The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and target size is estimated. Simulation results indicate that in ideal condition three sections of a ship can be tracked and ship's size can be estimated even though one of three sections can not be observed.

Estimation of boundary parameters and prediction of transmission loss based upon ray acoustics

Estimation of boundary parameters and prediction of transmission loss using a coherent channel model based upon ray acoustics and sound propagation data collected in field experiments are presented. Comparison between the prediction results and the experiment data indicates that the adopted sound propagation model is valuable, both selection and estimation methods on boundary parameters are reasonable, and the prediction performance of transmission loss is favorable.

Trabecular spacing estimation based on quadratic transformation algorithm

The quadratic transformation method is proposed to estimate the trabecular spac- ing （Tb.Sp）, an important index for osteoporosis diagnosis. The performance of this algorithm was investigated by scatter model, two-dimension finite difference time domain （2D-FDTD） simulation and in vitro experiments of bovine cancellous bone specimens. Comparing with the other four methods autoregressive cepstrum （AR）, adaptive filter- autoregressive cepstral （AFAR）, inverse filter-autoregressive eepstrum （InvAR）, and simplified inverse filter tracking （SIFT）, quadratic transformation is much more stable and accurate. The results demonstrated that quadratic transformation is a great algorithm for Tb.SD estimation.

Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.

Estimation method of glottal vocal efficiency based on conversion function of voice source

This investigation was designed to approach a novel estimation method of glottal vocal efficiency (GVE) based on conversion function of voice source. The conversion function of voice source was defined the ratio of supra-glottal acoustic voice source signal to the glottal air volume flow velocity waveform in frequency domain. A carefully designed in vivo canine larynx experiment and several human experiments including different vowels, pressed, falsetto, breath and typical laryngeal diseases were adopted to demonstrate this alternative GVE method. Compared with other vocal efficiency, it is shown that this method could eliminate the contribution from the super vocal tract transmission and resonance to GVE, and reflect the differences of phonation modes. The average magnitude of this conversion function in frequency domain represents GVE, and the variation of the magnitude in fundamental frequency is identical to AC/DC value.