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Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, single device sound scene classification has achieved good results, however, progress in multi-device sound scene classification has been slow. To solve the problem of large differences in the number of multi-device samples, this paper proposed a pairwise feature fusion algorithm. By calculating the differences in the spectrogram for each pair of paired samples and averaging these differences after accumulation, obtaining the average spectral characteristics of each device for the conversion of device samples. The algorithm effectively improved the generalization ability of the model while increasing the number of device samples. Meanwhile, in order to obtain global information, it proposed a lightweight attention module, which could make the model focus on the training of the whole sound sequence information on the basis of reduced computation by performing self-attention operations on the input features after compression in the frequency domain. The experimental results show that the proposed algorithm has better advantages in terms of model size and classification accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to apply neural network to urban LiDAR point clouds data classification, and to decrease large computation and long time consuming in the training process oflarge-scale point clouds data, this paper improved the original PointNet neural network by adding neighborhood characteristics of point clouds, and proposed a new classification algorithm for point clouds. This algorithm compressed the original point clouds data volume through grid clustering and resampling, and then it extracted multiscale neighborhood point clouds data and classified urban point clouds by using improved PointNet. This paper verified the classification algorithm with data from different regions. The results show that the algorithm has good classification effect, high classification accuracy and less calculation, and can realize effective classification of airborne LiDAR data in urban areas. [ABSTRACT FROM AUTHOR]

This paper made a comprehensive literature review on the topic of applying machine learning methods to shield tunnelling parameter predictions, stratum predictions ahead of a tunnel face, surface settlements prediction, shield attitude deviation and tool wear predictions. The selections of machine learning methods and the associated input and output parameters were analyzed, and the shortcomings and challenges of existing research were summarized. Some prospects were given, including the model generalization, multi-source heterogeneous data compression and assimilation, data-physics-based intelligent shield tunnelling, big data in shield tunnelling, in order to provide reference and guidance for the future research and engineering practice. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high learning cost and low compression efficiency caused by large data dispersion, small classification granularity and the defect of applied classification algorithm encountered in the compression process of the existing column-based database compression strategy, this paper designed a sorted-based hybrid compression strategy of column-based compression and sector-based compression. Firstly, it designed a method to sort the data in each column according to the characteristics of HBase to strengthen the data compaction. Secondly, according to the characteristics of the data, it applied the hybrid column-based compression strategy and the hybrid sector-based compression strategy respectively to recommend the compression algorithm. This paper conducted experiments on TPC-DS standard data and the results demonstrate that the proposed strategy has excellent performance in both compression rate and compression/decompression time. [ABSTRACT FROM AUTHOR]

In order to solve the poor communication environment problem of agricultural machinery operation state data transmission caused by the unbalanced coverage of a mobile communication base station, a data filtering and data compression method based on an improved Huffman coding technique was proposed for data selecting, compression, transmission, parsing, and extracting in this paper. First, the agricultural machinery operation data types, exchange mode, and compression mode were defined. Then, data collection and exchange were realized based on a Compass/GPS dual-mode state data collection terminal. Finally, an improved Huffman coding technique was proposed. At present, most of the data transmission is using a compression-decompression method to ensure data integrity of data transmission, which can reduce the data traffic and save many communication costs, but its disadvantages are also obvious. The disadvantages are fewer on the terminal in the data transmission, and are mainly manifested in the need to compress the data of each group, leading to delayed transmission time, which will affect the platform performance. Considering the common problem of an agricultural machinery state data collection terminal and monitoring platform, a driving state data optimization method combined with platform data recognition was proposed in this paper, whose research has practical significance for the platform designing of data collection, transmission, and monitoring. To achieve data transmission and access, the design of a monitoring platform necessarily must use data extraction methods combined with terminal data optimization. That is, adding the terminal data processing optimization algorithm while designing a terminal data transmission module to overcome the pressure of the data management system for wireless communications and the corresponding pressure for a backstage management system. At the same time, adding data extraction and recognition algorithms while monitoring platform receiving data could optimize the expression of data and reduce the amount of data, so as to improve the efficiency of the whole system. Data compression and decompression testing results showed that it could obtain a 55.08% compression ratio with 412.56 kb transmission operation data length by the data acquisition cycle of 5 seconds, and the error rate of data transmission and data transmission packet loss rate were 2.47% and 4.18% respectively, while the error rate of data transmission and data transmission packet loss rate fell to 2.06% and 0.78% respectively at the same screening requirements of the selecting compression and transmission. This method can achieve the requirements of agricultural machinery operation state data compression and transmission. In the compression and transmission mode of single data packet with less data and short transmission time, it could obtain a lower transmission error rate and transmission packet loss rate. Moreover, the method has less computation and high compression efficiency characteristics, which are suitable for data transmission in the agricultural machinery operation area. [ABSTRACT FROM AUTHOR]

Traditional data update synchronization tends to update the entire file directly at a time or selectively update local changes. Although the latter method saves part of the communication bandwidth, but every time the file changes, no matter how much change is immediately updated, increasing the communication load, these two methods are not very good to meet the real-time requirements of the industrial Internet. Aiming at this problem, this paper proposed an end-to-end cloud collaborative data synchronization scheme for industrial Internet. In this scheme, it judged the threshold by calculating the difference file. If the value was within the threshold, the changed data blocks were encoded and compressed and uploaded to the corresponding cloud storage node for decompression storage. Otherwise, relevant information would be temporarily stored in the edge layer. Compared with RS encoding and Rsync differential algorithm, it can be seen that the proposed scheme improves the transmission efficiency of updated data, and improves the security through coding compression, and the overall performance is better than other methods. [ABSTRACT FROM AUTHOR]

Ultrasonic plane wave imaging is one of the hot topics in the study of ultrasound imaging, but the plane wave ultra-high frequency ultrasound has brought a large amount of data. If these data are transmitted directly through the hardware, it will not only bring a great burden on the hardware, but also put a requirement for the ability of the hardware processing data. In order to reduce the problem caused by the high frame frequency and large amount of data, the data compression methods are used to reduce the amount of data acquisition and transmission. In this paper both lossy compression and lossless compression are studied, where LZW compression algorithm is chosen to test lossless compression effect, and based on the plane wave characteristics, the frequency domain cutting compression algorithm is developed as a lossy compression method. In this paper, the LZW algorithm is implemented in FGPA, and it can achieve a certain compression ratio after testing the ultra sonic simulation data. Under the condition of known fractional bandwidth, it is found that the main bandwidth can be preserved in the spectrum, while the rest of the frequency signal has little effect on the image. By testing the ultrasonic simulation data, the frequency domain cutting algorithm can achieve high compression ratio, and the difference between the original image and the reconstructed image is relatively smal . So from the point of the compression ratio, the frequency domain cutting algorithm is the primary choice of the ultrasonic data compression algorithm.

In order to improve the performance of neural network classifier, this paper proposed a segmented sample data selection method based on K-means clustering. First, K-means clustering was used to cluster the training samples according to the number of known categories, and then it compared the samples before and after clustering to find out the sample sets with clustering errors and the correct sample sets. Second, this method sorted the correct sample set of clustering and divided it into five segments according to the distance from each sample to the clustering center and selected the samples of odd segments and the samples of clustering errors to form a new training sample set. The proposed method could select informative data, delete redundant data, reduce the number of training data set and improve the quality of training data set. This method used some classifiers based on neural network combing the artificial data set and UCI data sets to study. Experimental results show that the performance of the three neural network classifiers is improved while the average compression ratio of the training samples is 66. 93% . [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low-dose computed tomography (CT) reconstruction has become the focus of X-ray CT imaging study. In this paper, we propose an improved prior image constrained compressed sensing (PICCS) reconstruction approach. A penalized weighted least-squares approach was adopted to realize the line integral projection (sinogram) data restoration, followed by filtered back-projection (FBP) of the restored sinogram data for image reconstruction. Finally, the FBP image as the prior image was used for PICCS approach for dose reduction. Qualitative and quantitative evaluations were carried out with computer simulation. The results showed that the present approach yielded noticeable gains over the original PICCS approach for dose reduction in terms of noise-induced artifacts suppression and edge detail preservation.

Lossless data compression systems are prone to transmission errors, and the effect of bit errors presents a spreading trend as the decoding progresses, which limits its applicability in the areas such as noisy wireless channels and file systems that may be damaged. Lossless data compression algorithm LZW can be widely used in file formats such as GIF, PDF, and TIFF. In this paper, the redundancy of the LZW compressed data was used to propose a design scheme of the error repair algorithm, and present the relevant theoretical basis. This scheme was arranged to be compatible with the standard LZW algorithm, meaning that the files compressed by the proposed scheme could still be decompressed by the standard LZW decoder. Experimental results verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The electric power information system is usually used to control the power equipment. The anomaly detection of electric power information system is very important for maintaining the stable operation of power equipment. However, the traditional anomaly detection method is difficult to detect the comprehensive anomalies of multi-indicator in the electric power information system. In order to solve this problem, an anomaly detection method based on improved A;-means algorithm is presented in this paper. To reduce the amount of calculation, the data space is compressed by dividing the data space into multiple grids and all the sample points in same grid are mapped by the mean point of grid. In order to accurately identify the normal mode, the accuracy of A;-means algorithm is improved by the mechanism of moving cluster boundaries based on the cluster boundary density and cluster density. Then the anomalies are detected by calculating the deviation degree between the data and normal mode. The experimental results show that the proposed method can accurately mine the comprehensive anomalies on multi-indicator. Compared with other anomaly detection methods, the running time of our method is reduced from 16.44 seconds to 0.55 seconds and the accuracy of anomaly detection is improved by 5.2%. Our method has good application prospects in the field of power operation and maintenance anomaly detection. [ABSTRACT FROM AUTHOR]

This paper introduces the implement method of DICOM medical image compression technology, The image part of DICOM files are extracted and converted to BMP format. The non-image information in DICOM file are stored into the text. When the final image of JPEG standard and non-image information are encapsulated to DICOM format images, it realizes the compression of medical image, which is beneficial to the image storage and transmission.

This paper combined the coded excitation with the technique of ophthalmic high-frequency ultrasonic imaging, and raised a new high-velocity data detection and signal processing method. 16 bit Golay complementarity sequences were produced by FPGA, which excited the probe to produce ultrasonic waves. Data detection circuit achieved the digitalized of the 15 MHz high-frequency ultrasonic echo signals in real time. Sampling rate was 120MHz, and sampling bit was 14 bit. Decoded compression arithmetic was realized by FPGA in real time, in which A code compression was alternated with B code compression. The return echo was correlated with the corresponding decode filter A and filter B respectively, and then the echo was delayed and summed to complete the decoding process. The experiment indicated that coded excitation can effectively increase the range of mainlobe and decrease the range of sidelobe. This technique can effectively raise the signal-to-noise ratio, and has significant research value in advancing the ophthalmic ultrasonic image quality and raising the safety of the equipment.

This paper attempts to study the key technology of network voice access devices with focus on core modules, i. e. voice AD/ DA conversion and network interface, according to the background of network voice communication in low altitude air traffic control(ATC). The G. 729A voice encode and decode algorithm is optimized both in C and assembly language and flow chart of voice data processing is given. Four channels voice compression coding and twenty channels decode processing are also realized in real-time by single chip TMS320C6455DSP. The network multi-channel voice communication is realized with TCP / IP and multi-channel voice interpolation is solved with solutions accordingly. The test reveals that less than 10 ms time delay of the voice processing device is achieved, which meets the real-time voice transmission demand of multi-subscriber. [ABSTRACT FROM AUTHOR]

Ever-increasing bandwidth has been utilized in wideband radar systems and the real-time implementation of pulse compression processing puts forward the need for ultra long points FFT technique. In this paper, a method of fast implementation of ultra long points FFT is proposed. Based on a certain high-performance FPGA processing platform and enlightened by the 2-D FFT algorithm, the ultra long FFT computation can be split into two sub-transform-blocks of FFTs with much fewer points each and external memory chips have to be employed to make up the shortage of in-chip storage resources. To speed up the whole processing, multi-parallel processing structure is adopted so as to guarantee the 4 million points FFT operation can be finished in 5ms. Finally, the experiment result shows that on certain processing platform the method proposed in this paper is effective and efficient dealing with the implementation of ultra long points FFT in radar systems and provides appropriate solution to the real-time pulse compression issue [ABSTRACT FROM AUTHOR]

In view of the fact that the large quantities of data contained in the medical image do not have a favorable setting for effective storage and transmission in the existing network, we have developed a JPEG2000 DICOM image compression kit using VC++, and we have realized the medical image storage service according to the C-STORE rule of DICOM standard. The results showed that the medical image processed by JPEG2000 compression algorithm not only greatly saved the storage space, but also reduced the band width needed by transmission. In this paper is discussed the DICOM image compressed by JPEG2000 compression algorithm in DICOM strorage service application. The significance of JPEG2000 compression algorithm in the development of picutre archiving and communication systems (PACS) and telemedicine is also explained.

Now researches of ECG data compression mainly focus on compressing the ECG data of low sampling rate. A BW-based high sampling rate ECG data lossless compression algorithm is proposed in this paper. We apply difference operation to the original ECG data first and take part of the 16-bit binary differential value as 8-bit binary. Then the differential results are coded with the move-to-front coding method in order to make the same characters centralizing in a certain area. Last, we gain a high compression ratio by using the arithmetic coding method further. Our experimental results indicate that this is an efficient lossless compression method suitable for body surface ECG data as well as for heart ECG data. The average compression ratios come up to 3.547 and 3.608, respectively. By comparison with current ECG compression algorithms, our algorithm has gained much improvement in terms of the compression ratio, especially when applied to the high sampling rate ECG data.

A video image recorder to record video picture for wireless capsule endoscopes was designed. TMS320C6211 DSP of Texas Instruments Inc. is the core processor of this system. Images are periodically acquired from Composite Video Broadcast Signal (CVBS) source and scaled by video decoder (SAA7114H). Video data is transported from high speed buffer First-in First-out (FIFO) to Digital Signal Processor (DSP) under the control of Complex Programmable Logic Device (CPLD). This paper adopts JPEG algorithm for image coding, and the compressed data in DSP was stored to Compact Flash (CF) card. TMS320C6211 DSP is mainly used for image compression and data transporting. Fast Discrete Cosine Transform (DCT) algorithm and fast coefficient quantization algorithm are used to accelerate operation speed of DSP and decrease the executing code. At the same time, proper address is assigned for each memory, which has different speed;the memory structure is also optimized. In addition, this system uses plenty of Extended Direct Memory Access (EDMA) to transport and process image data, which results in stable and high performance.

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

The compression of medical image series is very important in telemedicine. The motion estimation plays a key role in the video sequence compression. In this paper, an improved square-diamond search (SDS) algorithm is proposed for the motion estimation of medical image series. The improved SDS algorithm reduces the number of the searched points. This improved SDS algorithm is used in wavelet transformation field to estimate the motion of medical image series. A simulation experiment for digital subtraction angiography (DSA) is made. The experiment results show that the algorithm accuracy is higher than that of other algorithms in the motion estimation of medical image series.

In this paper, digital watermarking and EEG compression are introduced firstly, and then a number of digital watermarking methods are explored to resolve the problem of integrality and authenticity in EEG compression. At last, the current state of this technique inside and outside country is summarized and future endeavors are discussed.

This paper proposed a detection method of image copy-move forgery based on the interest points detection and feature matching. First, it applied Harris operator to detect corners as interest points, then extracted five spacial average features in the neighborhood of which as the center to constitute feature vector, and finally recorded the occurrence frequency of position transfer vector to obtain matching interest points with thresholding processing, thus marked copy-move regions. Experimental results show that the algorithm can not only detect multi-region copy-move manipulation effectively, but also be able to effectively resist various tampering post-processing operations, including additive white Gaussian noise, JPEG compression, contrast, brightness and exposure adjustment and the hybrid operation of JPEG compression and noise addition. [ABSTRACT FROM AUTHOR]

The paper reports a kind of new compression method of ECG signal. The method is realized by means of wavelet packets transform based on best basis performed by Shanon-Weaker entropy criterion. The result of simulation shows that this is an efficient compression method characterized by larger compression ratio and less loss, and the original signal can be recovered well.

A fast technique for implementing Holter cardio real time data reduction based on pointer and dual data buffers is presented in this paper, and an example of its application is also given here.

Transient simulation is indispensable for capturing the periodic disturbance between the rotor and stator for turbomachine applications. In order to avoid resonance risk, the turbomachine systems generally have prime number of components between the rotor and stator, which makes the periodic boundary conditions failed and the transient simulation must be carried out including all the passages. However, prohibitive computing resources and too much time cost are required to obtain detailed accurate simulations for the conventional methods of modeling all the passage. In this paper, 3 numerical approaches, the full passages simulation, the PT(Profile Transform) method with only 1 passage, the FT(fourier transform) method with double-passage, were respectively used to numerically predict the hydraulic performance and loading for the Francis99 turbine based on the k-ω SST turbulent model. The PT method overcame the unequal pitch problem by scaling the flow profile across the blade row interfaces. In the Fourier Transformation method, a phase-shifted boundary condition with Fourier data compression was used to account for the unequal pitch between the blade rows passages, and the solution history of circumferential boundary and rotational boundary was reasonably reconstructed. During the transient numerical simulations, the inlet and outlet boundary were given by the method of Profile Boundary Conditions. The mass flow with the velocity vector direction was prescribed on the inlet section, and the outlet was treated as a static pressure outlet where a static gauge pressure was specified. Compared with the experimental tests, the 3 simulation methods could accurately predict the hydraulic efficiency and the average pressure at different locations, and the static pressure distribution on the mid-span plane and pressure loading on runner blade were comparable. The hydraulic efficiency extracted by FT and PT methods were both higher than the result of the full passages simulation, which was attributed to the fact that the 2 simulation methods only included part of the flow passages and didn’t consider the hydraulic loss of volute, fixed guide vane and draft tube. The temporal variation of blade force and torque generated by the FT method showed in good agreement with the full passages method, expect for small deviation in pulsating amplitude. However, the PT method showed larger discrepancy with the full passages results for both of pulsating frequency and amplitude. In term of spectrum characteristics of pressure fluctuation, the numerical results extracted by the FT method yielded to a very good validation with the full passages and the experimental obtainment. Due to variable scaling or stretching at the Rotor-Stator interface, the passing frequency of the blade was captured in both the static domain and the rotating domain, and the pressure amplitude of PT method was lower than that of the full passages and the experimental. In addition, the time cost ratio among the full passages, the FT method and the PT method was about 1:0.375:0.23. We concluded that the FT method was capable of predicting the hydraulic performance and pressure fluctuation well with less computing resources and was a good alternative compared to the conventional full passage method. [ABSTRACT FROM AUTHOR]

This paper proposed segmentation method optimized by ABC algorithm in ASCS algorithm for the problem of ASCS algorithm, which adopted uniform segmentation instead of optimal segmentation. The ASCS algorithm only considered an influencing factor and ignored the influence of other factors. The ratio of compressing long sequence of uneven distribution was unsatisfactory with ASCS algorithm, it adopted the process integer sequence with sorting and differential encoding before ASCS algorithm. Simulation experiments show that the improved algorithm has significantly increased compression ratio of inverted index file comparing with ASCS algorithm. [ABSTRACT FROM AUTHOR]

After analyzing platform-Spark and the projection tree algorithm, this paper proposed the projection tree algorithm based on Spark( PTBS). In view of the shortcomings of the serial algorithm in dealing with large-scale data, the method had been improved in many aspects: to reduce the cost caused by the support count of candidate itemsets, changed the data storage structure; data was grouped by prefix and divided into different computing nodes to compress the searching space of data and realized parallel computation. Finally it implemented the algorithm on the Spark platform. Experiments show that the algorithm can run efficiently when dealing with massive data, and has good expansibility in the face of large scale data growth. [ABSTRACT FROM AUTHOR]

In view of the characteristics of electric energy data collected in power system, this paper puts forward the LZ77 power data compression algorithm based on integer wavelet transformation theory and LZ series compression algorithm. Aiming at the periodicity of power system data, the algorithm chooses a suitable wavelet base,according to its multi-resolution analysis, the algorithm transforms the power data into a wavelet domain and decomposes the signal into low-frequency components and high-frequency components. Low-frequency information of great value is compressed using LZ77 lossless compression,while high-frequency components are treated by the threshold quantization. Through the simulation test, it is proved that this algorithm has a higher compression ratio and a smaller reconstruction error, which is an effective real-time data compression method. [ABSTRACT FROM AUTHOR]

This paper proposed a data set compression algorithm based on nearest neighbor similarity to solve the problem that the clustering algorithm is loo expensive in the large data clustering analysis. It greatly reduced the size of the data set by dividing several data points nearest to each other into a data cluster and forming new data set with randomly selecting cluster heads. Then it used the K-means algorithm and the AP algorithm to cluster the compressed datasets respectively. The experimental results show that compared with the original data set clustering analysis, the compressed dataset can reduce the time of clustering and improve the clustering performance of the algorithm in the case of the clustering accuracy is basically the same, which proves that the validity and reliability of data set compression algorithm in cluster analysis. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most important design aspects for high-end GPU is improving its power efficiency. Data compression can be used in various stages of the GPU 3D rendering pipeline to reduce off-chip memory traffic, yield high performance and low power consumption. In order to summarize the research progress and application status of data compression techniques for GPU pipeline, this paper outlined the key features of data compression specialized for buffer objects and texture based on the structure characteristics of GPU 3D rendering pipeline and memory system. Moreover, it discussed research status, inefficiencies and challenges of data compression in GPU pipeline. In the end, it indicated the further research content for GPU data compression according to application demands. [ABSTRACT FROM AUTHOR]

In real-time rendering, the burden of data transi miss ion, data storage, data processing influence the real-time rendering speed. Based on this, this paper proposed the algorithm of embedded zerotree coding based on fast DCT and LOD to realize progressive compression and render the terrain in real time. First, it used fast discrete cosine transform ( DCT) to process the terrain data, then clustered the coefficients. Finally, it used the embedded zero tree coding to process the coefficients. The experiments show that this algorithm can compress terrain data efficiently, reduce the burden of computer, increase the frame rate of 20% , which is in line with visual feature. [ABSTRACT FROM AUTHOR]

Focused on the defect that compression artifacts in low-bit MPEG images sequence,this paper proposed a post-processing technique of regularization to decompress MPEG objection. Firstly,the quantization interval of the DCT coefficients of video original could be gotten because of adaptive quantization,and the reconstructed video sequences were projected onto the interval. Secondly,because of videos were continuous still images sequences,it presented two total variation models that all dimensions were coupled and temporal dimension was decoupled from two spatial dimensions. Finally,it exploited a classical primal-dual algorithm to solve the convex model so as to obtain a post-processing MPEG video sequences. The experimental results demonstrate that the total variation functions can reduce compression artifacts to some extent and improve the quality of the decoded videos. [ABSTRACT FROM AUTHOR]

For the real-time imaging sonar, the imaging data require to be compressed to transmit to the littoral combat ship (LCS) in some cases such as in the wireless communication between a LCS and a remote multi-mission vehicle ( RM-MV). In this paper, a discrete cosine transform ( DCT) and subsection quantization based imaging data compression method was proposed, which could apply to the real-time sonar imaging row data. The steps of the method were as follows. Firstly the row data were transformed by DCT. Secondly the DCT sequence was truncated for its strong "energy compaction" property. Finally the truncated data were divided into several subsections, each of which was quantized separately. In the display end, which usually located in a LCS, after an adverse processing, the decompressed imaging raw data would display on the screen in real time. The method was applied to the lake trial sonar data, indicating this effective solution. [ABSTRACT FROM AUTHOR]

In the traditional qualitative trend analysis, the width of segments cannot be adaptive, the threshold is dependent on human experience, and the algorithm is complicate. Due to these shortcomings, this paper proposed a new qualitative trend analysis method with a sliding window. In the method, data in a sliding window was fitted linearly. According to the result of linear fitting, it expanded or reduced the sliding window to determine the best width of a segment. When all the segments were determined, they were identified as increasing, decreasing or steady. Finally it obtained the qualitative trend of data. Its application on chemical data trend analysis proves that the method can extract and identify qualitative trend effectively with higher accuracy and lower complexity. It can lay a solid foundation for data compression and fault diagnosis. [ABSTRACT FROM AUTHOR]

This paper developed a multiple description video coding scheme based on 3D-SPIHT to solve the problem that receiving terminals were unable to decode because of the partial loss of coded video bit-stream transmitted over unreliable networks like ZigBee. Compared with traditional MDC methods like MDSQ, the descriptions created by this scheme were basically equal with each other and could improve compression ratio by cancelling frame redundant. Compared with international standard like H. 264, this scheme had some advantages, such as the algorithm was much more simple, the bit-stream could be decoded at any bit, the bit rate was scalable, and the reconstruction images had no clearly block effecting. The experiments show that this practical coding scheme being implemented on ZigBee multi-channel network will obtain good reconstruction images at a high compression ratio. [ABSTRACT FROM AUTHOR]

Coron et al applied batch technique to the fully homomorphic encryption scheme over the integers, which could ho-momorphicly process and encrypt a plaintext vector in a ciphertext and improved the efficiency of the original scheme. But it' s public-key size was Õ(λ8). In order to reduce the public-key size, combined with the quadratic forms of public key elements and the ciphertext compression technique, this paper presented a variant batch fully homomorphic encryption scheme over the integers, which the public-key size was Õ(λ8). The scheme had smaller public-key size. Finally, it proves that the scheme is semantically secure. [ABSTRACT FROM AUTHOR]

To solve the problem that the existing affine interest point detection algorithms cannot directly determine the characteristic scale for interest point, this paper proposed an invariant interest point detector based on Gabor multiscale-space. Firstly, this algorithm built the Gabor multiscale-space representation by smoothing the image with a series of Gabor filters. Secondly, it used the maxima criterion to detect the interest points and determine the characteristic scale. Finally, it described the local structure shape of interest point by the second moment matrix. The experimental data demonstrate that the proposed detector obtains the best performance under image blur and JPEG compression in terms of repeatability and matching score, compared to other detectors, such as Hessian-Affine and MSER et al, and then it is an effective interest point detection algorithm with determining the characteristic scale directly. [ABSTRACT FROM AUTHOR]

The problems of privacy preserving, trajectory data simplification and online processing attract considerable efforts from researchers in the area of trajectory data mining, unfortunately, it is difficult for traditional method to solve all these problems. This paper proposes an online similar trajectories mining method which can preserve the privacy of original trajectory data. The method first compressed and perturbed the original data based on random projection technique, then found the similar moving objects in each time segment by clustering the transformed data based on density, and finally it found similar trajectories by estimating that if the trajectories were similar for a long enough duration and estimated the similarity of trajectories using local sensitivity hashing. This avoided the intersection operation in traditional method and reduced the computation time. The experimental results show that this method can find similar trajectories effectively and reduce the cost of computation time. [ABSTRACT FROM AUTHOR]

The transmission rate of uplink and downlink has been increasing with the application of Long Term Evolution (LTE), and correspondingly the amount of data transferred by distributed base station has been growing. But nowadays, the used equipment can only support a limited interface rate. Aiming at this problem, we could compress data before transmission. This paper refer to synthetic aperture radar data compression algorithm--the block adaptive quantization compression algorithm. We will improve the algorithm and the algorithm is applied to the base station data compression. In our method, in-phase and quadrature data (IQ data) is firstly partitioned, and we make the data block to the standard normal distribution. Secondly, we could use the optimum threshold value to encode the block data. Simulation results show the block adaptive quantization compression algorithm can compress the IQ data bits effectively. [ABSTRACT FROM AUTHOR]

This paper proposed a novel algorithm, in which the data of block regions mapped to the corresponding sub-band coefficients in order to make the image compression and encryption realized simultaneously and improve security. It used the key to modify the original context. And then it sent the modified context and the corresponding decision to arithmetic coder to implement the joint compression-encryption. The security encryption by the adaptive arithmetic coding is better than the one by arithmetic coding based on interval splitting (ACIP). The distribution of the adaptive arithmetic coding is more complex than the probability distribution of ACIP which is fixed. It simulated the proposed algorithm. The results show that the scheme has the same compression efficiency compared with the original image compression algorithm. The relative quality of different regions can be controlled flexibly. It can have a better information security efficiency to use the way of image block. Different keys can be used to encrypt the coefficients of different regions, which can realize the important information privacy and confidentiality protection. [ABSTRACT FROM AUTHOR]