A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Left Ventricular Systolic Intraventricular Flow Field Assessment in Hyperthyroidism Patients Using Vector Flow Mapping

Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological condition. This study examined use of new VFM and flow field for assessment of left ventricular （LV） systolic hemodynamics in patients with simple hyperthyroidism （HT）. Thirty-seven simple HT patients were enrolled as HT group, and 38 gender- and age-matched healthy volunteers as control group. VFM model was used to analyze LV flow field at LV apical long-axis view. The follow- ing flow parameters were measured, including peak systolic velocity （Vs）, peak systolic flow （Fs）, total systolic negative flow （SQ） in LV basal, middle and apical level, velocity gradient from the apex to the aortic valve （AV）, and velocity according to half distance （V1/2）. The velocity vector in the LV cavity, stream line and vortex distribution in the two groups were observed. The results showed that there were no significant differences in the conventional parameters such as left ventricular ejection fraction （LVEF）, left ventricular end-diastolic diameter （LVEDD） and left atrium diameter （LAD） between HT group and control group （P〉0.05）. Compared with the control group, a brighter flow and more vortexes were detected in HT group. Non-uniform distribution occurred in the LV flow field, and the stream lines were discontinuous in HT group. The values of Vs and Fs in three levels, SQ in middle and basal levels, AV and V1/2 were higher in HT group than in control group （P〈0.01）. AV was positively correlated with serum free thyroxin （FT4） （r=0.48, P〈0.01）. Stepwise multiple regression analysis showed that LVEDD, FT4, and body surface area （BSA） were the influence factors of △V. The unstable left ventricular sys- tolic hydrodynamics increased in a compensatory manner in simple PIT patients. The present study in- dicated that VFM may be used for early detection of abnormal ventricle contraction in clinical settings.

Wavelet neural network based watermarking technology of 2D vector maps

A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.

Information hiding scheme for vector maps based on fingerprint certification

An information hiding scheme for vector maps is presented to identify the source after the vector map is leaked in some key application areas. In this scheme, the fingerprint image of the map owner can be converted into a character string as the watermark, and then the watermark will be embedded into the coordinate descriptions of the attribute file by the "0-bit value" programming method. This programming algorithm ensures that the accuracy is lossless and the graphics is unchanged for any vector map. Experiments show that the presented hiding scheme has stable robustness, the average similarity rate is 97.2% for fingerprints matching and the false non-match rate is 1.38% in the blocking test. In the opening test, the former reaches 84.46% and the latter reaches 5.56%.

Precise Transceiver-Free Localization in Complex Indoor Environment

Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor(KNN) algorithm, the other is Support Vector Regression(SVR) algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times.

Development of Measurement System for Flow and Shape Using Array Ultrasonic Sensors

In Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, the fuel debris formed in the Reactor Pressure Vessel (RPV) and Primary Containment Vessel (PCV) at Unit 1 - 3. To accelerate and decide further decommissioning steps of the FDNPP, it is crucial to obtain realistic information of the debris and localize contaminated water leakage from PCV. Due to high radiation and dark environment inside the PCV, investigating instruments and techniques should necessarily to meet specification of radiation resistance, waterproofness, dust resistance and so on. This study focuses on development of ultrasonic measurement system using a couple of sectorial array sensors to localize contaminated water leakage and visualize shape of object that repre- senting fuel debris, simultaneously. In this study, Total Focusing Method (TFM) and Ultrasonic Velocity Profiler (UVP) methods are considered to visualize object shape and flow pattern around it, respectively. To demonstrate applicability and reliability of developed measurement system with sectorial array sensors, a mock-up experiment result of simulated water leakage and fuel debris shape were discussed in this paper.

Left ventricle energy loss in normo-and hypertensive subjects

Background Vector flow mapping is a novel echocardiographic technique that enables the visualization of the intraventricular flow. We aimed to evaluate and compare the index of hemodynamic dissipative energy loss in patients with hypertension and the ones with normotensive, unaffected control subjects. Methods & Results Transthoracic echocardiography was performed in eighty-nine hypertensive patients with preserved left ventricular ejection fraction, fifty-one hypertensive patients with left ventricular hypertrophy(LVH group) and thirty-eight hypertensive patients without LVH(non-LVH group). Forty-two healthy volunteers were enrolled as the control group. The stored images were analyzed to calculate the energy loss. The average energy loss of diastole in the LVH group was significantly increased(controls vs. non-LVH vs. LVH: 7.07 ± 0.91 vs. 12.44 ± 3.14 vs. 16.29 ± 3.17 J/s per m^3). Compared with the control group, the energy loss was significantly increased in the LVH group during the different periods in diastole. The energy loss in the non-LVH group was the greatest among the three groups during the atrial contraction period. Conclusions Energy loss provides a promising method for evaluating the energy efficiency in the left ventricle and may be a new indicator of left ventricular cardiac dysfunction.

A Fixed Point Theorem and Some Generalized Ky Fan's Minimax Inequalities

In this paper, we establish a fixed point theorem for set-valued mapping on a topological vector space without ＂local convexity＂. And we also establish some generalized Ky Fan＇s minimax inequalities for set-value vector mappings, which are the generalization of some previous results.

Super-resolution image reconstruction based on three-step-training neural networks

A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS

With the dramatic development of spatial data in- frastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great chal- lenge in how to improve performance. The real-time visual- ization of vector maps is the most common function in Cyber- GIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the effi- ciency of visualization of large vector maps is still a signif- icant research direction for GIScience scientists. In this re- search, we review the existing three optimization strategies, and determine that the third category strategy （i.e., parallel optimization） is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimiza- tion is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial hetero- geneous characteristic of vector data, we use a ＂horizontal grid, vertical multistage＂ approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds.Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the real- time visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.