A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

Exploration of French-Chinese Translation Methods of Electrical Engineering Terminology Using Online Image-Text Retrieval Mode

With the incessant propulsion of the Open Door Policy,which is related to the consolidation of international collaborative partnerships,an increasing number of Chinese companies are moving toward cooperating countries to participate in infrastructure construction,employing a win-win strategy in favor of the people and governments of both countries.Among the cooperation domains,our country’s electrical companies have achieved a series of remarkable results in the international Engineering,Procurement,and Construction(EPC)project market with their outstanding business capabilities and technical advantages.Nevertheless,some shortcomings cannot be overlooked,the most notable of which appears to be the impediment associated with engineering translation,which has always been an obsession among translators of Chinese companies.Taking the transmission line project in the Republic of Madagascar as an example,an analysis of French-Chinese translation methods of electrical engineering terminology in the field of the transmission line is carried out.

Travel time and alternative configurations analysis for automated storage/retrieval systems based on geometrical method

In order to evaluate the efficiency of the automated storage/retrieval system(AS/RS)accurately,and compare different layouts of the AS/RS using mean travel time,under randomized storage conditions,an exact,geometry-based analytical model is presented.The model can be used to compute the expected single-command and dual-command travel time for a storage/retrieval(S/R)machine which can travel simultaneously horizontally and vertically as it moves along a storage aisle.The rack may be either square in time or non square in time.Additionally,the alternative layouts of the AS/RS and travel-time models are examined.Comparing with setting the I/O point at the left-lower corner of the rack,setting the I/O point at any point at the vertical edge can help enhance the efficiency of the AS/RS.

High Efficient Methods of Content-based 3D Model Retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the ＂Shape Distribution （D2）＂ algorithm, and a new algorithm named ＂Quick D2＂ is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, ＂3D Searcher＂, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.

Study of the Simultaneous Physical Retrieval Method for Meteorological Parameters over the Continental Plateau of China

It is common knowledge that continental retrieval especially for Qinghai-Xizang Plateau has not been solved todate. In order to explore applicable inverse model and method for continent including the plateau, in this study authors use an improved simultaneous physical retrieval method hereafter referred to as the ISPRM, for computing meteorological parameters from NOAA-10 satellite TOVS data. The retrieval results verified by nearby radiosondesshow that the ISPRM is more applicable for the continental plateau.

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

Retrieval and Regional Distribution Analysis of Ammonia,Sulfur Dioxide and Nitrogen Dioxide in the Urban Environment Using Ultraviolet DOAS Algorithm

Aiming at the in situ and mobile observation of urban environmental air pollution,a portable instrument using ultraviolet spectrum retrieval algorithm was developed based on the basis of Differential Optical Absorption Spectroscopy(DOAS)and multiple-pass cell technique.Typical trace gas pollutants,NH3,SO2,and NO2,were explored using their optical spectral characteristics in deep ultraviolet wavelength range from 210 to 215 nm.The gas concentration was retrieved by Lambert-Beer’s law and nonlinear least square method.With an optimized optical alignment,the detection limits of NH3,SO2,NO2 were estimated to be 2.2,2.3,and 36.2 ppb,respectively.The system was used in carrying out some cruise observations in Chengdu,China.During the entire period,the polluted gases showed varied distribution and typical daily average concentrations ofNH3,SO2,NO2 were 23.2,3.5,and 106.0 ppb,respectively.The contributions from different sources were analyzed combined with the HYSPLIT model.Results show that the portable DOAS system is a convenient and effective tool for regional distribution measurement and pollution source monitoring.

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I.Sensitivity analysis and simulation experiments

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section （NRCS） or the method of wind retrieval？ It is valuable to research. First, depending on SDP2.0, the simulated ＇true＇ NRCS is calculated from the simulated ＇true＇ wind through the geophysical mode] function NSCAT2. The simulated background field is configured by adding a noise to the simulated ＇true＇ wind with the non-divergence constraint. Also, the simulated ＇measured＇ NRCS is formed by adding a noise to the simulated ＇true＇ NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational （2DVAR） ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.

Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.

An Improved Method for Doppler Wind and Thermo dynamic Retrievals

A variational method is developed to retrieve winds in the first step and then thermodynamic fields in the second step from Doppler radar observations. In the first step, wind fields are retrieved at two time levels: the beginning and ending times of the data assimilation period, simultaneously from two successive volume scans by using the weak form constraints provided by the mass continuity and vorticity equations. As the retrieved wind fields are expressed by Legendre polynomial expansions at the beginning and ending times, the time tendency term in the vorticity equation can be conveniently formulated, and the retrieved winds can be compared with the radar observed radial winds in the cost function at the precise time and position of each radar beam. In the second step, the perturbation pressure and temperature fields at the middle time are then derived from the retrieved wind fields and the velocity time tendency by using the weak form constraints provided by the three momentum equations. The merits of the new method are demonstrated by numerical experiments with simulated radar observations and compared with the traditional least squares methods which consider neither the precise observation times and positions nor the velocity time tendency. The new method is also applied to real radar data for a heavy rainfall event during the 2001 Meiyu season in China.

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.

A Three-Dimensional Satellite Retrieval Method for Atmospheric Temperature and Moisture Profiles

A three-dimensional variational method is proposed to simultaneously retrieve the 3-D atmospheric temperature and moisture profiles from satellite radiance measurements. To include both vertical structure and the horizontal patterns of the atmospheric temperature and moisture, an EOF technique is used to decompose the temperature and moisture field in a 3-D space. A number of numerical simulations are conducted and they demonstrate that the 3-D method is less sensitive to the observation errors compared to the 1-D method. When the observation error is more than 2.0 K, to get the best results, the truncation number for the EOF＇s expansion have to be restricted to 2 in the 1-D method, while it can be set as large as 40 in a 3-D method. This results in the truncation error being reduced and the retrieval accuracy being improved in the 3-D method. Compared to the 1-D method, the rms errors of the 3-D method are reduced by 48% and 36% for the temperature and moisture retrievals, respectively. Using the real satellite measured brightness temperatures at 0557 UTC 31 July 2002, the temperature and moisture profiles are retrieved over a region （20°-45°N, 100°- 125°E） and compared with 37 collocated radiosonde observations. The results show that the retrieval accuracy with a 3-D method is significantly higher than those with the 1-D method.

L1/2 -Regularized Quantile Method for Sparse Phase Retrieval

The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines the quantile regression and the L1/2-regularizer. It is a non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on the Alternating Direction Methods of Multiplier (ADMM) to solve the corresponding optimization problem. Numerous numerical experiments show that this method can recover sparse signals with fewer measurements and is robust to dense bounded noise and Laplace noise.

Probabilistic 2D Shape Retrieval and Applications via the Method of Hurwitz-Radon Matrices

Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. Proposed method is based on the family of MHR which possess columns composed of orthogonal vectors. 2D curve is retrieved via different functions as probability distribution functions： sine, cosine, tangent, logarithm, exponent, arcsin, arccos, arctan and power function. Created from the family of N-1 MHR and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are： accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O（L）, MHR interpolation is not a linear interpolation.

Temporally Modulated Phase Retrieval Method for Weak Temporal Phase Measurement of Laser Pulses

We propose a simple iterative algorithm based on a temporally movable phase modulation process to retrieve the weak temporal phase of laser pulses. This unambiguous method can be used to achieve a high accuracy and to simultaneously measure the weak temporal phase and temporal profile of pulses, which are almost transform- limited. A detailed analysis shows that this iterative method has valuable potential applications in the charac- terization of pulses with weak temporal phase.

Designing a Document Retrieval Method for University Digital Libraries Based on Hadoop Technology

With the development of big data,all walks of life in society have begun to venture into big data to serve their own enterprises and departments.Big data has been embraced by university digital libraries.The most cumbersome work for the management of university libraries is document retrieval.This article uses Hadoop algorithm to extract semantic keywords and then calculates semantic similarity based on the literature retrieval keyword calculation process.The fast-matching method is used to determine the weight of each keyword,so as to ensure an efficient and accurate document retrieval in digital libraries,thus completing the design of the document retrieval method for university digital libraries based on Hadoop technology.

Parameterized first-guess spectrum method for retrieving directional spectrum of swell-dominated waves and huge waves from SAR images

A method to retrieve ocean wave spectra from SAR images, named Parameterized First-guess Spectrum Method （PFSM）, was proposed after interpretation of the theory to ocean wave imaging and analysis of the drawbacks of the retrieving model generally used. In this method, with additional information and satellite parameters, the separating wave-number is first calculated to determine the maximum wave-number beyond which the linear relation can be used. The separating wave-number can be calculated using the additional information on wind velocity and parameters of SAR satellite. And then the SAR spectrum can be divided into SAR spectrum of wind wave and of swell according to the result of separating wave-number. The portion of SAR spectrum generated by wind wave, is used to search for the most suitable parameters of ocean wind wave spectrum, including propagation direction of ocean wave, phase speed of dominating wave and the angle spreading coefficient. The swell spectrum is acquired by directly inversing the linear relation of ocean wave spectrum to SAR spectrum given the portion of SAR spectrum generated by swell. We used the proposed method to retrieve the ocean wave spectrum from ERS-SAR data from the South China Sea and compared the result with altimeter data. The agreement indicates that the PFSM is reliable.

A Method for Retrieving Water-leaving Radiance from Landsat TM Image in Taihu Lake, East China

The visible and infrared bands of Landsat Thematic Mapper （TM） can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660rim. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band （2.1 μm）, and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S （Second Simulation of the Satellite Signal in the Solar Spectrum） code based on standard atmospheric and aerosol models.