Test Data Compression Using Selective Sparse Storage

For today’s very large scale integrated circuits, test data volume is recognized as a major contributor to the cost of manufacturing testing. Test data compression addresses this problem by reducing the test data volume without affecting the overall system performance. This paper proposes a new test data compression technique using selective sparse storage. Test sets are partitioned into four kinds of blocks of uniform length, all-0 blocks, all-1 blocks, sparse blocks and characterless blocks. Blocks are encoded appropriately based on the occurrence of them. They are encoded into 0, 10, 110 + number of the sparse bits + locations of all the sparse bits, and 111 + the block itself, respectively. Two algorithms are proposed for how to select the sparse blocks from test sets. A theoretical analysis for our selective sparse storage shows the new compression technique outperforms the conventional test data compression approaches. Experimental results illustrate the flexibility and efficiency of the new method, which is consistent with the theoretical analysis.     

Test Data Compression Using Selective Encoding of Scan Slices

We present a selective encoding method that reduces test data volume and test application time for scan testing of Intellectual Property (IP) cores. This method encodes the slices of test data that are fed to the scan chains in every clock cycle. To drive $N$ scan chains, we use only $c$ tester channels, where $c=lceillog_2(N+1)rceil+2$ . In the best case, we can achieve compression by a factor of $N/c$ using only one tester clock cycle per slice. We derive a sufficient condition on the distribution of care bits that allows us to achieve the best-case compression. We also derive a probabilistic lower bound on the compression for a given care-bit density. Unlike popular compression methods such as Embedded Deterministic Test (EDT), the proposed approach is suitable for IP cores because it does not require structural information for fault simulation, dynamic compaction, or interleaved test generation. The on-chip decoder is small, independent of the circuit under test and the test set, and it can be shared between different circuits. We present compression results for a number of industrial circuits and compare our results to other recent compression methods targeted at IP cores.      

分形插值图象放大和压缩编码

本文讨论了随机分形插值方法及其在图象放大和图象压缩编码中的应用。实验结果表明，用分形插值方法实现图象放大和压缩编码能获得良好的结果。     

一种基于正交小波包变换的数据压缩方法

本文将最优小波包基变换应用到数据压缩方法的研究工作中,针对不同频率的小波包系数之不同统计特性设计了相应的标量量化和矢量量化方法,并对一组实际测量数据运用我们建议的方法进行压缩,在失真率较小的情况下,得到较高的压缩比.     

基于Memetic优化的智能DNA序列数据压缩算法

提出近似重复矢量(Approximate Repeat Vector,ARV)模型用于DNA序列冗余片段的描述.通过将数据生物信息学特征引入压缩预处理,并使用ARV矢量构造编码码本,提出了非对称DNA序列压缩算法BioLZMA-2.算法引入基于粒子群优化的Memetic改进方法CLIPSO-MA用于压缩码本的智能优化设计,有效提升了编码性能.在标准测试序列上的实验结果表明,BioLZMA-2可获得比现有DNA序列数据压缩方法更高的压缩率.     

第二代小波的心电压缩算法

针对心电数据的特殊性，提出一种实用的心电数据无损压缩算法，在获得较大的压缩率的同时保证压缩和解压的速度。     

基于小波包变换的SAR原始数据压缩

该文介绍了小波包变换和网格编码量化的优点,结合合成孔径雷达(SAR)原始数据的特点,提出了基于小波包变换和网格编码量化的SAR原始数据压缩算法。利用模拟和实际数据分析了该压缩算法的性能,通过与其他原始数据压缩算法的比较说明了这一方法的有效性。     

Test Data Compression Using Multi-dimensional Pattern Run-length Codes

Test data compression is an efficient methodology in reducing large test data volume for system-on-a-chip designs. In this paper, a variable-to-variable length compression method based on encoding runs of compatible patterns is presented. Test data in the test set is divided into a number of sequences. Each sequence is constituted by a series of compatible patterns in which information such as pattern length and number of pattern runs is encoded. Theoretical analyses on the evolution of the proposed Multi-Dimensional Pattern Run-Length Compression (MD-PRC) are made respectively from one-Dimensional-PRC to three-Dimensional-PRC. To demonstrate the effectiveness of the proposed method, experiments are conducted on both larger ISCAS’89 benchmarks and the industrial circuits with large number of don’t cares. Results show this method can achieve significant compression in test data volume and have good adaptation to industrial-size circuits.     

Optimal Selective Count Compatible Runlength Encoding for SOC Test Data Compression

Test data volume amount is increased multi-fold due to the need of quality assurance of various parts of the circuit design at deep submicron level. Huge memory is required to store this enormous test data which not only increases the cost of the ATE but also the test application time. This paper presents an optimal selective count compatible run length (OSCCPRL) encoding scheme for achieving maximum compression for reduction of the test cost. OSCCPRL is a hybrid technique that amalgamates the benefits of other two techniques: 10 Coded run length (10 C) and Selective CCPRL (SCCPRL) proposed here. These techniques work on improvement of the 9 C and CCPRL techniques. In OSCCPRL, entire data is segmented in blocks and further compressed using inter block and intra block level merging techniques. SCCPRL technique is used for encoding the compatible blocks while the 10C is used to do encoding at sub block (half block length) level. In case, if no compatibility is found at block/sub block level then the unique pattern is held as such in the encoded data along with the necessary categorization bits. The decompression architecture is described and it is shown how by just the addition of few states of FSM, better test data compression can be achieved as compared to previous schemes. The simulation results performed for various ISCAS benchmarks circuits prove that the proposed OSCCPRL technique provides an average compression efficiency of around 80 %.     

一种方向插值预测变长编码的帧存有损压缩算法

为了提高帧存储的压缩性能,该文提出一种基于方向插值预测变长编码(DIPVLC)的帧存有损压缩算法。首先根据自适应纹理方向插值获取参考像素,从而得到预测残差,然后优化率失真模型对预测残差进行量化,最后通过游程哥伦布算法对量化残差进行变长编码。实验结果显示,与内容感知自适应量化(CAAQ)的帧存压缩算法相比,该文算法不但PSNR下降更少,而且压缩率提高了10.05%,同时编码时间减少了10.62%。     

基于多项式插值的外测数据拼帧及平滑问题研究

针对微波统一测控系统外测测量数据的插值对齐、拼帧与平滑计算问题,结合工程实际情况,探讨了基于多项式模型的计算方法,给出了多项式的选取建议,以及测角等类型测元数据处理的较高精度计算方法。仿真计算结果表明提出的方法可行,不同的插值方法、多项式选取和角度处理方法等对精度有较大影响,在具体的计算中需要综合考虑精度及实时性要求的限制。研究结果对外测测量数据的处理有一定的参考价值。     

Data Compression Using Adaptive Coding and Partial String Matching

The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during the course of the transmission, and has been shown to incur a smaller coding overhead than explicit transmission of the model's statistics. But there is a basic conflict between the desire to use high-order Markov models and the need to have them formed quickly as the initial part of the message is sent. This paper describes how the conflict can be resolved with partial string matching, and reports experimental results which show that mixed-case English text can be coded in as little as 2.2 bits/ character with no prior knowledge of the source.     

采用字典词条衍生模式的测试数据压缩

为了降低数字集成电路测试成本,压缩预先计算的测试集是一种有效的解决途径。该文根据索引位数远少于字典词条,以及测试数据中存在大量无关位,提出一种采用词条衍生和二级编码的字典压缩方案。该方案引入循环移位操作,确保无关位按序任意移动而不丢失,从而扩大词条衍生性能,减少非词条向量个数。另外,采用规则的两级编码可以减少码字数量和解压电路的复杂度。实验结果表明该文所提方案能够进一步提高测试数据压缩率,减少测试时间。     

High-Order Supervised Discriminant Analysis for Visual Data

In practical applications, we often have to deal with high-order data, for example, a grayscale image and a video clip are intrinsically a 2nd-order tensor and a 3rd-order tensor, respectively. In order to satisty these high-order data, it is conventional to vectorize these data in advance, which often destroys the intrinsic structures of the data and includes the curse of dimensionality. For this reason, we consider the problem of high-order data representation and classification, and propose a tensor based fisher discriminant analysis(FDA), which is a generalized version of FDA, named as GFDA. Experimental results show our GFDA outperforms the existing methods, such as the 2-directional 2-dimensional principal component analysis((2D)2PCA), 2-directional 2-dimensional linear discriminant analysis((2D)2LDA), and multilinear discriminant analysis(MDA), in high-order data classification under a lower compression ratio.     

High-Order Supervised Discriminant Analysis for Visual Data

In practical applications, we often have to deal with high-order data, for example, a grayscale image and a video clip are intrinsically a 2nd-order tensor and a 3rd-order tensor, respectively. In order to satisty these high-order data, it is conventional to vectorize these data in advance, which often destroys the intrinsic structures of the data and includes the curse of dimensionality. For this reason, we consider the problem of high-order data representation and classification, and propose a tensor based fisher discriminant analysis (FDA), which is a generalized version of FDA, named as GFDA. Experimental results show our GFDA outperforms the existing methods, such as 2-directional 2-dimensional principal component analysis ((2D)2PCA), 2-directional 2-dimensional linear discriminant analysis ((2D)2LDA), and multilinear discriminant analysis (MDA), in high-order data classification under lower compression ratio.     

基于光谱特征编码的快速矢量量化三维谱象数据压缩

本文用矢量量化技术将成象光谱仪的三维谱象数据空间景象中每一个象元对应的光谱定义为一个矢量，用基于光谱特征的二进制光编编码方法对各光谱矢量进行编码，用编码后的光谱矢量来进行快速码字匹配，这种三维谱象数据压缩方法不公认吏处理速度大大加快（当码书取２５６码字时比常规的矢量量化方法快３０倍，码书取４０９６码字时快４３倍）而且压缩后恢复图象精度还有所提高，本文定义的矢量构成方法不仅可有效地保存光谱特性，而且     

On Using Exponential-Golomb Codes and Subexponential Codes for System-on-a-Chip Test Data Compression

We examine the use of exponential-Golomb codes and subexponential codes can be used for the compression of scan test data in core-based system-on-a-chip (SOC) designs. These codes are well-known in the data compression domain but their application to SOC testing has not been explored before. We show that these codes often provide slighly higher compression than alternative methods that have been proposed recently.     

分形插值图像压缩的一种快速解码算法

一维分形插值图像编码是用插值点数据构造分形曲线来拟合数字图像的灰度曲线从而实现压缩。其解码过程就是求用插值点数据构造的迭代函数系统(IFS)的吸引子,由于图像数据以及分形插值迭代规律的特殊性,使得随机迭代算法和通常的固定迭代算法并不适用。本文设计了快速且节省内存的解码算法,并进行了复杂度分析。同时,本文的算法作为分形插值方法的一部分,同样可以用在分形插值法的其他应用领域。     

小波变换的SAR原始数据网格编码量化压缩算法

介绍了小波变换和网格编码量化的优点,针对SAR原始数据的基本特点,提出了基于小波变换的8状态网格编码量化压缩算法。对小波变换后的数据进行网格编码量化,利用卷集编码和信号空间扩展来增大量化信号间的欧氏距离,获得了更好的量化效果。利用实际数据进行的实验结果表明,文中提出的算法在量化性能上优于已有的各种压缩算法。