Memory deduplication techniques are widely used to improve memory utilization in cloud computing platforms, and they can be categorized into virtualized and containerized environments. In virtualized environments, prevalent memory deduplication approaches often rely on scanning the virtual address space of different processes. However, the complexity of virtual address spaces can reduce scanning efficiency in containerized environments. Additionally, the many-to-one mapping between virtual and physical pages can decrease the efficiency of merging operations.To solve the above problems, we proposed a Container-based Kernel Samepage Merging method called CKSM. This method leverages potential duplicate candidates and efficiently performs merging operations. It employs layered sampling to construct the priority of physical pages. Additionally, a physical page scanning mechanism is designed to directly obtain valid pages within the system. CKSM uses the physical page merge mechanism to merge all virtual pages at once and release the corresponding memory directly. We conduct several experiments to demonstrate the efficiency of CKSM. It reduces the scanning overhead by up to 80.99% and increases page comparison efficiency by up to 42.51%. Besides, CKSM achieves an average of 3.02×memory usage reduction compared to UKSM and 2.79×response speedup compared to KSM in the containerized environment. In cloud computing emulation, CKSM has been proven to be optimal in high-density deployment.

Data deduplication is an effective technique to reduce the amount of redundant data, which is widely used in backup systems. To reconstruct the original backup data, restore is a typical procedure in inline deduplication, which brings read amplification when duplicate chunks are shared among various data streams. Rewrite is a cost-efficient method to improve the inline deduplication restore performance by writing the fragmented duplicate chunks repeatedly. Although several rewrite schemes are proposed to improve the restore performance, they either decrease the deduplication ratio or increase the temporal overhead of the rewrite procedure. This is because existing rewrite methods select inappropriate number of containers, or ignore the inter-container redundancy information. To address the above problems, we propose an E ffective –Region -Partitioning based rewrite scheme (ERP), which improves the restore performance in backup systems and ensures a high deduplication ratio. The key idea of ERP is to effectively narrow the selection range and choose a flexible number of containers by investigating the inter-container redundancy information. To demonstrate the effectiveness of ERP, we conduct several experiments in a deduplication backup system. Compared to the state-of-the-art rewrite schemes, the results show that ERP reduces the rewrite cost by up to 97.71%.

Within the cloud computing scenario, each server usually carries multiple service processes, which intensifies the concurrency pressure of the system. As a result, the process of memory management during page allocation and deallocation becomes a significant bottleneck. Although several methods such as Buddy System and Inverse Buddy System (iBuddy) have been proposed to improve the performance of memory management, they cannot adapt to the highly concurrent environment of cloud computing, because they either force the memory allocation/deallocation requests to be serialized or bring extra fragmentation. To address the above problem, we propose Spring Buddy, which improves the concurrency of both memory allocation and deallocation and avoids unnecessary fragmentation. It can detect the changes of system- and process-level memory request patterns and dynamically adjust the organization of page frames. Inventively, Spring Buddy uses the spring core layer to provide both concurrent response and resource aggregation capability which is adapted to the system's concurrency pressure, and also uses the spring lazy layer to further mitigate the system resource contention through process behavior prediction. To demonstrate the effectiveness of Spring Buddy, we implement it in the Linux kernel. The results demonstrate that Spring Buddy can reduce memory allocation latency by 71.47 % and deallocation latency by 93.20% on average compared to the existing methods.

Selection of a reference image is an important step in color balancing. However, the past research and currently available methods do not focus on it, leading to the lack of an effective way to select the reference image for color balancing in remote sensing imagery mosaic. This letter proposes a novel automatic reference image selection method that aims to select the reference images by assessing multifactors according to the land surface types of the target images. The proposed method addresses the limitations caused by the use of a single assessment factor as well as the selection of a single image as the reference in traditional methods. In addition, the proposed method has a wider range of applications than those requiring no reference image. The visual experimental results indicate that the proposed method can select the suitable reference images, which benefits the color balancing result, and outperforms the other comparative methods. Moreover, the absolute mean value of skewness metric of the proposed method is 0.0831, which is lower than the values of the other comparison methods. It indicates that the result of the proposed method had the best performance in the color information. The quantitative analyses with the metric of absolute difference of mean value indicate that the proposed method has a good ability in maintaining the spectral information, and the spectral changing rates had been reduced at least 10.66% by the proposed method when compared with the other methods.

ZY-3, which was launched on January 9, 2012, is the first stereo mapping satellite in China. The initial accuracy of direct georeferencing with the onboard three-line camera (TLC) imagery is low. Sensor geometric calibration with bundle block adjustment is used to improve the georeferencing accuracy. A new on-orbit sensor calibration method that can correct the misalignment angles between the spacecraft and the TLC and the misalignment of charge-coupled device is described. All of the calibration processes are performed using a multistrip data set. The control points are automatically matched from existing digital ortho map and digital elevation model. To fully evaluate the accuracy of different calibration methods, the calibrated parameters are used as input data to conduct georeferencing and bundle adjustment with a total of 19 strips of ZY-3 TLC data. A systematic error compensation model is introduced as the sensor model in bundle adjustment to compensate for the position and attitude errors. Numerous experiments demonstrate that the new calibration model can largely improve the external accuracy of direct georeferencing from the kilometer level to better than 20 m in both plane and height. A further bundle block adjustment with medium-accuracy ground control points (GCPs), using these calibrated parameters, can achieve external accuracy of about 4 m in plane and 3 m in height. Higher accuracy of about 1.3 m in plane and 1.7 m in height can be achieved by bundle adjustment using high-accuracy GCPs.

Overparameterization is one of the major problems that the rational function model (RFM) faces. A new approach of RFM parameter optimization is proposed in this paper. The proposed RFM parameter optimization method can resolve the ill-posed problem by removing all of the unnecessary parameters based on scatter matrix and elimination transformation strategies. The performances of conventional ridge estimation and the proposed method are evaluated with control and check grids generated from Satellites d'observation de la Terre (SPOT-5) high-resolution satellite data. Experimental results show that the precision of the proposed method, with about 35 essential parameters, is 10% to 20% higher than that of the conventional model with all 78 parameters. Moreover, the ill-posed problem is effectively alleviated by the proposed method, and thus, the stability of the estimated parameters is significantly improved.

A high squint subaperture (HSS) algorithm was developed from the perspective of a strip map synthetic aperture radar (SAR) system and has the ability to focus SAR data at extremely high squint angle of 55/spl deg/ with less than 1.27% mainlobe expansion or even higher squint angles if larger mainlobe expansion can be tolerated. The unique characteristic of this algorithm is that the subapertures are formed by multiplying the received signal with a set of overlapped complex conjugated reference signals where the chirp rate varies in the azimuth direction. This is done to solve the range focus problem that arises after the azimuth input signal undergoes the range walk removal procedure. As the IISS algorithm only involves short fast Fourier transforms (FFTs) and avoids interpolation, it is computationally efficient and allows for small data buffers and facilitates hardware real-time implementation. The distinct feature of this algorithm is its simplicity of implementation, which is vital in real time processing and motion compensation when the squint angle may be changing continuously.

A new technique for inversed synthetic aperture radar (ISAR) ranging, which resembles the principle of the Vernier measuring system, is presented. In this technique, the transmitted ISAR pulse comprises a train of chirp subpulses with uniformly stepped up center frequencies. The return ISAR echo is first processed, using hardware, to determine a coarse estimate of the target range. Further refinements of the range estimate are achieved through software processing, consisting of two stages of discrete Fourier transform operation. The ranging accuracy can be increased without the need for increased bandwidth, but at the expense of a slight increase in computational complexity. Numerical evaluation shows that a noiseless system is capable of achieving high-ranging accuracy, of the order of millimeters, even in the presence of dispersion and target motion. From computer simulations, the proposed system is also found to be robust against additive system noise and frequency jitter under practical conditions.

High-squint angle synthetic aperture radar (SAR) imaging has applications in specific situations such as providing looking angle-dependent scattering information and revisiting an area of interest. It is very difficult for current SAR-imaging algorithms to be used directly in high-squint configurations. This is due to the large range-cell migration and the nonnegligible cubic-phase term in the signal. In this paper, an improved algorithm called the time-varying step-transform algorithm is proposed. It uses different chirp rates to deramp the signal in different subapertures to alleviate the range-focus problem. The interval between subapertures is also varied according to the deramping chirp rate. The nonnegligible cubic-phase term is included in the focusing procedure. The performance of the proposed algorithm in high-squint-angle mode is excellent. By reducing the subaperture length as the squint angle is increased, the performance of the algorithm is almost independent of squint angle. The proposed algorithm is also flexible for multilook processing and motion-error compensation.

A 3D terrain reconstruction method using compound techniques is proposed. Normal matching results only supply a DSM (digital surface model). This means that the matching results may be on the top of objects such as houses or trees. This kind of matching results could not supply an accurate DEM (digital elevation model). The proposed method is a more efficient method for determining elevations from overlapping digital aerial images and satellite images. It combines image analysis and image matching methods and supplies a more accurate DEM.

One of the major research areas in computer vision and digital photogrammetry is image matching for the reconstruction of a Digital Elevation Model (DEM). Image matching is the first and difficult step in terrain reconstruction. DEMs produced by digital photogrammetric workstations supply important information on land form for cartographers and Geographic Information System (GIS) developers and users. Since the accuracy of maps, orthophotographs and GIS databases must be within a specified range for appropriate analysis of the information and subsequent decision making. An accurate DEM is needed. Current image matching methods for DEM determination only supply a Digital Surface Model (DSM). This means matching results map be on top of objects such as buildings, or trees. This does not supply an accurate DEM. A compound approach for 3D terrain reconstruction is proposed. It combines image analysis and image matching methods and supplies a more accurate DEM.

A new theory which describes the transient response of a PLL to a periodic ramp voltage applied to the VCO for generating a coherent chirp signal has been presented. With the appropriate choice of loop parameters and the ramp voltage rate to make the value of the normalized loop natural frequency within the range (0, 1), the PLL's equation can be simplified to a nearly linear differential equation. Its asymptotic solution can be obtained by the perturbation method as ε is a rather small value. Computer simulation results in agreement with the analysis have been obtained. Its application to generate a coherent linear frequency modulation waveform using a high-gain second-order PLL is considered.