An Improved HVQ Algorithm for Compression and Rendering of Space Environment Volume Data with Multi-correlated Variables

Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.

Compression of the North Hemisphere derived from space geodesy

The convergent and divergent velocities of active plate boundaries in the North Hemisphere are obtained with space geodetic data. The relative motions of adjacent plates in north-south direction are almost convergent; the spreading rates of the north mid-Atlantic ridge are smaller than the south mid-Atlantic ridge; the closed differences of the baseline length rates between stations on different plates along the latitudinal circle of 7.7, 23.3? 34.8? 42.0?and 51.0?are all negative. All these show that the North Hemisphere is a compressive hemisphere.

An Algorithm to Reduce Compression Ratio in Multimedia Applications

In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the world.The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual quality.During transmission,this massive data necessitates a lot of channel space.In order to overcome this problem,an effective visual compression approach is required to resize this large amount of data.This work is based on lossy image compression and is offered for static color images.The quantization procedure determines the compressed data quality characteristics.The images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being used.In the transform domain,the color planes are encoded using the proposed quantization matrix.To improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image block.We used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB conversion.Peak to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed images.With the relevant transforms,the image size and bits per pixel are also explored.Using the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression ratio.Because of these characteristics,multimedia systems and services have a wide range of possible applications.

Feature Patch Illumination Spaces and Karcher Compression for Face Recognition via Grassmannians

Recent work has established that digital images of a human face, when collected with a fixed pose but under a variety of illumination conditions, possess discriminatory information that can be used in classification. In this paper we perform classification on Grassmannians to demonstrate that sufficient discriminatory information persists in feature patch (e.g., nose or eye patch) illumination spaces. We further employ the use of Karcher mean on the Grassmannians to demonstrate that this compressed representation can accelerate computations with relatively minor sacrifice on performance. The combination of these two ideas introduces a novel perspective in performing face recognition.

Remote sensing image compression for deep space based on region of interest

A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.

An image compression method for space multispectral time delay and integration charge coupled device camera

Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.

WELL-POSEDNESS IN CRITICAL SPACES FOR THE FULL COMPRESSIBLE MHD EQUATIONS

In this paper we prove local well-posedness in critical Besov spaces for the full compressible MHD equations in R^N, N≥ 2, under the assumptions that the initialdensity is bounded away from zero. The proof relies on uniform estimates for a mixed hyperbolic/parabolic linear system with a convection term.

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.

Lossless compression of digital mammography using base switching method

Mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. Archiving and retaining these data for at least three years is expensive, diffi-cult and requires sophisticated data compres-sion techniques. We propose a lossless com-pression method that makes use of the smoothness property of the images. In the first step, de-correlation of the given image is done using two efficient predictors. The two residue images are partitioned into non overlapping sub-images of size 4x4. At every instant one of the sub-images is selected and sent for coding. The sub-images with all zero pixels are identi-fied using one bit code. The remaining sub- images are coded by using base switching method. Special techniques are used to save the overhead information. Experimental results indicate an average compression ratio of 6.44 for the selected database.

Compressive sampling and reconstruction in shift-invariant spaces associated with the fractional Gabor transform

In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.

Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images.Therefore,image compression techniques can be utilized to process remote sensing images.In this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image construction.The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it.With this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing images.The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook.Besides,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is employed.The design of SBO algorithm for remote sensing image compression depicts the novelty of the work.To showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

First Order Fuzzy Transform for Images Compression

In this paper, we present a new image compression method based on the direct and inverse F1-transform, a generalization of the concept of fuzzy transform. Under weak compression rates, this method improves the quality of the images with respect to the classical method based on the fuzzy transform.

Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis

This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.

压缩感知和图卷积神经网络相结合的宽频振荡扰动源定位方法

新能源并网引发的宽频振荡严重威胁电网安全,实现宽频振荡源的在线定位并及时采取抑制措施以保证系统安全稳定尤为必要。为此,提出一种压缩采样和图卷积神经网络相结合的宽频振荡源定位方法,该方法首先在子站对时序的振荡信号进行稀疏采样,获得其低维观测序列,作为节点的时序信息,然后在主站融合系统的拓扑结构捕捉各节点的邻接关系,综合考虑系统振荡的时空特性,运用图卷积神经网络实现振荡源定位。最后利用宽频振荡样本集进行仿真验证,结果表明所提方法在量测数据含有噪声、传输数据缺失以及传输数据偏差的情况下都有较高的定位准确度。

OTFS系统SBL-Turbo压缩感知信道估计算法

针对正交时频空调制(OTFS)系统由多普勒频移引起的信道估计准确度下降的问题,本文提出了一种联合无线信道在时延-多普勒域稀疏特性的SBL-Turbo压缩感知信道估计算法。首先,对时延-多普勒域稀疏信道建模,使其服从以噪声功率为条件的高斯先验分布,利用稀疏贝叶斯学习模块估计得到稀疏信道的均值与方差,并结合期望最大化算法更新高斯先验模型中的参数。其次,引入了LMMSE(线性最小均方误差)估计器模块,该模块对稀疏信道的后验分布进行再估计,提高估计的准确度。通过对每个模块估计得到的信道后验分布进行数据处理,使得模块的输入值与输出值解耦,进而减少模块间的错误传播。最后,两个模块采用Turbo结构迭代估计信道的后验分布,得到信道状态信息。实验结果表明,相较于其他估计方法,该算法能够显著提高信道估计的精度,并且改善系统的误码率性能,能够有效地解决OTFS系统中由多普勒频移引起的信道估计问题。

废弃矿地下空间储能方案及性能

为高效利用废弃煤矿地下空间,提出了废弃矿地下巷道储存热能和电能的方案。基于气水互驱原理并利用沉降区人工湖,提出了定压压缩空气储能的新思路以高效利用地下空间并提高储能性能,构建了废弃地下巷道储热、抽水蓄能、定容和定压压缩空气储能的数学模型并进行了数值求解,分析了储热和储电性能,对比了储能密度和能量回收效率,探索了定压压缩空气储能的高值化利用方式。结果表明:①地下巷道储热可解决太阳能光热的不稳定性和跨季节储存难题,热回收效率大于98%,储能密度为653.42 kJ/(m^(3)·d^(-1)),折算电能为0.18 kW·h/(m^(3)·d^(-1));②地下巷道抽水蓄能、定容和定压压缩空气储能的能量回收效率和储能密度分别为70.56%、57.76%、67.64%和1.14、2.25、5.56 kW·h/(m^(3)·d^(-1)),因残余气少,储释能过程总压比和膨胀比不变,定压压缩空气储能性能优异;③通过地上发电地下储能,可将废弃矿场打造为集发电储电和冷热淡冰联产于一体的新能源供应中心。研究成果可为废弃矿地下空间储能工程提供决策依据,为可再生能源配储提供支撑。

间歇式信息传输条件下无人机搜索覆盖规划

在基站通信范围受限条件下,若无人机(unmanned aerial vehicle,UAV)执行覆盖搜索任务时经常返回至基站通信范围内实现间歇式信息传输,能够扩展其覆盖区域和提高执行任务的灵活性。为最小化所有环境位点信息传回基站的时间之和,需解决覆盖规划和间歇式通信时机选择的耦合问题。在覆盖的目标点较少且分散时,采用改进的层次聚类方法求解每次往返需要覆盖的路径点集合。在需要进行区域全覆盖时,则在求解完区域的覆盖路径后,以最小化时间之和为目标,对目标函数进行分析,确定最优返回次数的搜索范围,压缩解空间。对该搜索范围进行遍历搜索得到最优往返次数,然后利用遗传算法优化UAV返回位点。与前沿算法对比,所提算法在目标函数和覆盖路径质量上具有一定的提升。

增材制造VNbTiSi轻质难熔共晶高熵合金的组织及力学性能

高熵合金与增材制造技术的结合,为极端服役环境下结构复杂部件的一体化制造提供了新的思路。采用激光熔化沉积(LMD)技术成功制备了VNbTiSi轻质难熔共晶高熵合金,通过显微组织分析筛选出最佳激光功率参数,并对试样进行了室温及高温压缩性能测试。结果表明:VNbTiSi轻质难熔共晶高熵合金表现出了优异的打印性能,最佳工艺参数下制备得到的样品在宏观和微观上均没有出现裂纹。在合金底面及沿构建方向,熔池内部与熔池边界(搭接处)均呈现出不同的形貌,熔池内部由柱状的全共晶组织构成,共晶胞为熔池边界出现较为粗大的(Nb, X)5Si3初生硅化物相。相比铸态组织,激光熔化沉积使得共晶组织的片层间距显著细化。增材制造合金不仅在1000℃下压缩强度可达640 MPa,在1100℃时依然能够保持高于500 MPa的压缩强度,高温压缩性能显著优于铸态VNbTiSi合金。

基于COMGRU的AUV航路轨迹预测方法

针对采用神经网络预测自主水下机器人航迹存在滞后性的问题,本文提出一种基于信息压缩的改进门控循环神经网络,用于水下自主机器人航路多步轨迹预测。该算法将水下自主机器人航行轨迹附近的障碍物位置信息、海流信息以及时空轨迹信息共同构成的地理位置信息进行数据压缩处理,作为本文预测网络的输入,以提高网络训练效率。实验验证该算法减少了水下自主机器人航迹多步预测的滞后性且具有较高的准确率。

评价磁共振3D-SPACE序列及VIBE序列对三叉神经微血管压迫的诊断效能及应用价值

目的:探讨磁共振3D-SPACE及VIBE序列对三叉神经微血管压迫的诊断效能及实际应用价值。方法:对18例单侧原发性三叉神经痛患者同时行磁共振3D-SPACE及VIBE序列检查,所有患者均经三叉神经微血管减压术证实。分析三叉神经微血管压迫的影像学特征,评价其显示三叉神经脑池段与周围血管关系的能力和优势。结果:18例患者中,症状侧血管神经Ⅰ型3例(16.6%),Ⅱ型12例(66.6%),Ⅲ型3例(16.6%);而无症状侧Ⅰ型12例(66.7%),Ⅱ型6例(33.3%),Ⅲ型0例;双侧压迫程度差异显著,有统计学意义(P=0.000)。在有血管神经接触、压迫情况的神经中,症状侧15例中近端压迫9例,远端压迫6例;无症状侧6例中近端压迫4例,远端压迫2例,症状侧与非症状侧压迫点位置差异无统计学意义(P=0.328)。症状侧小脑上动脉为主要接触、压迫血管(58%)。3D-SPACE及VIBE序列能对三叉神经进行多平面重建,显示血管压迫三叉神经的位置、程度及责任血管的来源。结论:3D-SPACE序列能清晰显示三叉神经与周围结构的关系,VIBE序列是三叉神经MR成像常用的补充序列,3D-SPACE与VIBE序列相结合能提供准确的诊断信息,对原发性三叉神经痛的诊断具有重要价值。