Faults, as unique geological structures, disrupt the mechanical connections between rock masses. During coal mining, faults in the overlying strata can disturb the original stress balance, leading to fault activation and altering the typical subsidence patterns. This can result in abnormal ground deformation and significant damage to surface structures, posing a serious geological hazard in mining areas. This study examines the influence of a known fault (F13 fault) on ground subsidence in the Wannian Mine of the Fengfeng Mining Area. We utilized 12 Sentinel-1A images and applied SBAS-InSAR, StaMPS-InSAR, and DS-InSAR time-series InSAR methods, alongside the D-InSAR method, to investigate surface deformations caused by the F13 fault. The monitoring accuracy of these methods was evaluated using leveling measurements from 28 surface movement observation stations. In addition, the density of effective monitoring points and the relative strengths and limitations of the three time-series methods were compared. The findings indicate that, in low deformation areas, DS-InSAR has a monitoring accuracy of 7.7 mm, StaMPS-InSAR has a monitoring accuracy of 16.4 mm, and SBAS-InSAR has an accuracy of 19.3 mm.

本文在道格拉斯-普克法的基础上提出利用逐点前进法对点云数据进行精简,以兔子的模型为对象进行试验研究.结果表明,逐点前进法对点云数据精简在速度和精简率上与道格拉斯-普克法相比有明显提高.

Device authentication and key exchange are essential issues for Internet of Things (IoT) which plays an indispensable role in modern life. Many sensor devices in IoT, with limited resources and poor self-protection capabilities, are deployed in the unattended and open places, making them vulnerable to physical attacks while facing traditional security threats. Despite several researches have been conducted by using the physical unclonable function (PUF) to immune the communication between IoT devices from the security threats above, as per the knowledge of the authors, current solutions rely on the participation of the server to distribute the key parameters, which requires high message overhead and markedly influences the efficiency. To fill this gap, this article proposes an end-to-end mutual authentication and key exchange protocol for IoT by combining PUF with certificateless public key cryptography (CL-PKC) on elliptic curve, which only needs “three handshakes” without the real-time participation of the server. The security analysis shows that the proposed protocol can not only secure the IoT devices from various attacks, but also provide perfect forward secrecy. Moreover, the experimental validation and performance analysis show the proposed protocol outperforms existing related protocols in terms of security features, protocol rounds and communication cost.

Light field microscopy (LFM) is an emerging technology for high-speed wide-field 3D imaging by capturing 4D light field of 3D volumes. However, its 3D imaging capability comes at a cost of lateral resolution. In addition, the lateral resolution is not uniform across depth in the light field dconvolution reconstructions. To address these problems, here, we propose a snapshot multifocal light field microscopy (MFLFM) imaging method. The underlying concept of the MFLFM is to collect multiple focal shifted light fields simultaneously. We show that by focal stacking those focal shifted light fields, the depth-of-field (DOF) of the LFM can be further improved but without sacrificing the lateral resolution. Also, if all differently focused light fields are utilized together in the deconvolution, the MFLFM could achieve a high and uniform lateral resolution within a larger DOF. We present a house-built MFLFM system by placing a diffractive optical element at the Fourier plane of a conventional LFM. The optical performance of the MFLFM are analyzed and given. Both simulations and proof-of-principle experimental results are provided to demonstrate the effectiveness and benefits of the MFLFM. We believe that the proposed snapshot MFLFM has potential to enable high-speed and high resolution 3D imaging applications.

根据总体最小二乘模型的高斯-牛顿解法,建立了病态加权总体最小二乘模型的平差准则.由拉格朗日乘数法导出了病态加权总体最小二乘模型的高斯-牛顿正则化迭代解,在等权情形下导出了其与一般正则化解的近似差异.最后用两个算例对算法的有效性进行了验证,结果表明最小二乘解和总体最小二乘解受设计阵病态性影响而严重偏离真值,且病态性对总体最小二乘解的影响远大于最小二乘解;高斯-牛顿正则化迭代法同时考虑了设计阵和观测值的误差,并引入正则化参数削弱了设计阵的病态性,其解的精度较最小二乘解和总体最小二乘解大幅度提升.

利用截断奇异值解法处理了病态加权总体最小二乘模型,详细推导了参数的截断奇异值解及其偏差、方差以及均方误差公式,该算法无需迭代求解,易于实现.将截断奇异值解的均方误差与最小二乘解的方差进行比较,发现当奇异值由大到小依次变化时,截掉奇异值所造成的解的均方误差下降量的符号由负逐渐变正,由此导出了确定截断参数的公式.数值算例和病态测边网算例分析结果表明,受模型病态性的影响,最小二乘解和总体最小二乘解的精度较差;截断奇异值解能够有效地削弱模型的病态性同时又顾及了系数阵的误差,其解的精度最高.

Accurate measurements of the geometric shape and the internal structure of cultural artifacts are of great importance for the analysis and understanding of artworks such as paintings. Often their complex layers, delicate materials, high value and uniqueness preclude all but the sparsest sample-based measurements (microtomy or embedding of small chips of paint). In the last decade, optical coherence tomography (OCT) has enabled dense point-wise measurements of layered surfaces to create 3D images with axial resolutions at micron scales. Commercial OCT systems at biologically-useful wavelengths (900 nm to 1.3 mu m) can reveal some painting layers, strong scattering and absorption at these wavelengths severely limits the penetration depth. While Fourier-domain methods increase measurement speed and eliminate moving parts, they also reduce signal-to-noise ratios and increase equipment costs. In this paper, we present an improved lower-cost time-domain OCT (TD-OCT) system for deeper, high-resolution 3D imaging of painting layers. Assembled entirely from recently-available commercially-made parts, its 2x2 fused fiber-optic coupler forms an interferometer without a delicate, manually-aligned beam-splitter, its low-cost broadband Q-switched super-continuum laser source supplies 20 KHz 0.4-2.4 mu m coherent pulses that penetrate deeply into the sample matrix, and its single low-cost InGaAs amplified photodetector replaces the sensitive spectroscopic camera required by Fourier domain OCT (FD-OCT) systems. Our fiber and filter choices operate at 2.0 +/- 0.2 mu m wavelengths, as these may later help us characterize scattering and absorption characteristics, and yield axial resolution of about 4.85 mu m, surprisingly close to the theoretical maximum of 4.41 mu m. We show that despite the moving parts that make TD-OCT measurements more time-consuming, replacing the spectroscopic camera required by FD-OCT with a single-pixel detector offers strong advantages. This detector measures interference power at all wavelengths simultaneously, but at a single depth, enabling the system to reach its axial resolution limits by simply using more time to acquire more samples per A-scan. We characterize the system performance using material samples that match real works of art. Our system provides an economical and practical way to improve 3D imaging performance for cultural heritage applications in terms of penetration, resolution, and dynamic range.

Temporal Video Frame Synthesis (TVFS) aims at synthesizing novel frames at timestamps different from existing frames, which has wide applications in video codec, editing and analysis. In this paper, we propose a high framerate TVFS framework which takes hybrid input data from a low-speed frame-based sensor and a high-speed event-based sensor. Compared to frame-based sensors, event-based sensors report brightness changes at very high speed, which may well provide useful spatio-temoral information for high framerate TVFS. In our framework, we first introduce a differentiable forward model to approximate the physical sensing process, fusing the two different modes of data as well as unifying a variety of TVFS tasks, i.e., interpolation, prediction and motion deblur. We leverage autodifferentiation which propagates the gradients of a loss defined on the measured data back to the latent high framerate video. We show results with better performance compared to state-of-the-art. Second, we develop a deep learning-based strategy to enhance the results from the first step, which we refer as a residual "denoising" process. Our trained "denoiser" is beyond Gaussian denoising and shows properties such as contrast enhancement and motion awareness. We show that our framework is capable of handling challenging scenes including both fast motion and strong occlusions.

Volumetric biological imaging often involves compromising high temporal resolution at the expense of high spatial resolution when popular scanning methods are used to capture 3D information. We introduce an integrated experimental and image reconstruction method for capturing dynamic 3D fluorescent extended objects as a series of synchronously measured 3D snapshots taken at the frame rate of the imaging camera. We employ multifocal microscopy (MFM) to simultaneously image at 25 focal planes and process this depth-encoded image to recover the 3D structure of extended objects, such as bacteria, using a sparsity-based reconstruction approach. The combined experimental and computational method produces image quality similar to confocal microscopy in a fraction of the acquisition time. In addition, our computational image reconstruction approach allows a simplified MFM optical design by correcting aberrations using the measured response to point sources. This "compressive" MFM acquisition and reconstruction method, where an image volume with roughly 8 million voxels is recovered from a single 1-megapixel captured image, enables straightforward study of dynamic processes in 3D, and as a simultaneous snapshot advances the state of the art in dynamic 3D microscopy.

We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z-axis, but the result suffers from poor resolution in the z-axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.

We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ 1 -norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates. However, each iteration of IPM is more expensive than in ISTA because it needs to evaluate an inverse of a Hessian matrix to compute the Newton direction. We propose to approximate each Hessian matrix by a diagonal matrix plus a rank-one matrix. This approximation matrix is easily invertible using the Sherman-Morrison formula, and is used as a novel preconditioner in a preconditioned conjugate gradient method to compute a truncated Newton direction. We demonstrate the efficiency of our algorithm in compressive 3D volumetric image reconstruction. Numerical experiments show favorable results of our method in comparison with previous interior point based and iterative shrinkage/thresholding based algorithms.

Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope," in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits optical elements for correcting chromatic aberrations and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve spatial resolutions of 0.35um (lateral) and 0.5um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at 25 frames per second using our proposed computational multifocal microscopy technique.

Multi-focus microscope (MFM) provides a way to obtain 3D information by simultaneously capturing multiple focal planes. The naive method for MFM reconstruction is to stack the sub-images with alignment. However, the resolution in the z-axis in this method is limited by the number of acquired focal planes. In this work we build on a recent reconstruction algorithm for MFM, using information from multiple frames to improve the reconstruction quality. We propose two multiple-frame MFM image reconstruction algorithms: batch and recursive approaches. In the batch approach, we take multiple MFM frames and jointly estimate the 3D image and the motion for each frame. In the recursive approach, we utilize the reconstructed image from the previous frame. Experimental results show that the proposed algorithms produce a sequence of 3D object reconstruction with high quality that enable reconstruction of dynamic extended objects.

We propose an imaging architecture to achieve high spatial resolution ToF imaging via optical multiplexing and compressive sensing. We developed a prototype 1-megapixel compressive ToF camera that achieves as much as 4 × improvement in spatial resolution.

Realizing both high temporal and spatial resolution across a large volume is a key challenge for 3D fluorescent imaging. Towards achieving this objective, we introduce an interferometric multifocus microscopy (iMFM) system, a combination of multifocus microscopy (MFM) with two opposing objective lenses. We show that the proposed iMFM is capable of simultaneously producing multiple focal plane interferometry that provides axial super-resolution and hence isotropic 3D resolution with a single exposure. We design and simulate the iMFM microscope by employing two special diffractive optical elements. The point spread function of this new iMFM microscope is simulated and the image formation model is given. For reconstruction, we use the Richardson-Lucy deconvolution algorithm with total variation regularization for 3D extended object recovery, and a maximum likelihood estimator (MLE) for single molecule tracking. A method for determining an initial axial position of the molecule is also proposed to improve the convergence of the MLE. We demonstrate both theoretically and numerically that isotropic 3D nanoscopic localization accuracy is achievable with an axial imaging range of 2um when tracking a fluorescent molecule in three dimensions and that the diffraction limited axial resolution can be improved by 3-4 times in the single shot wide-field 3D extended object recovery. We believe that iMFM will be a useful tool in 3D dynamic event imaging that requires both high temporal and spatial resolution.

Reactive Jamming attack could severely disrupt the communications in ZigBee networks, which will have an evident jamming effect on the transmissions in a hard-to-detect manner. Therefore, after analyzing the general process of reactive jamming, we develop a lightweight reactive jammer localization scheme, called IndLoc, which is applicable to ZigBee networks. In this scheme, we first design the time-varying mask code (TVMC) to protect the transmission of the packets to ensure that the jammer cannot monitor the channel effectively. Then, the strength of jamming signal (JSS) could be collected by sending inducing messages into the channel. And the location of the jammer can be estimated through the locations of JSS peak nodes, which are selected according to the gradient ascent algorithm. Experiments are performed based on an open-source stack, msstatePAN. And the results reveal that IndLoc could effectively protect the transmissions of the packets and achieve relatively higher localization accuracy under different network scenarios with fewer calculation and storage overheads.

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate 10× temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.