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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.

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.

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.

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.

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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.)

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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 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]

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]

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 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]

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]

Specific to the feature of seismic data signal and noise, and combining the embedded zerotree code, this paper proposes an encoding algorithm of seismic data compression based on the lifting wavelet transform; this algorithm uses the lifting scheme of Daubechies9/7, and codes the coefficient by improved SPIHT. The results of the method are obtained for real seismic data. The experiment indicates that the program can code the large seismic data quickly, achieve the high fidelity compression of seismic data, and has well practical value. [ABSTRACT FROM AUTHOR]

In order to improve the data coding rate and transmission security, according to the inverse function of iteration skew tent map, which could be used as arithmetic coding, this paper propsed a compression and encryption scheme based on the piecewise linear chaotic map. It solved the separate problems of encoding and encryption in the existing program. The simulation results indicate that the algorithm improves the coding efficiency and unpredictability of ciphertext. At the same time, it can effectively resist the attack of selective plaintext, short plaintext attack and brute force search attack. [ABSTRACT FROM AUTHOR]