ADVANCED FREQUENCY-DIRECTED RUN-LENTH BASED CODING SCHEME ON TEST DATA COMPRESSION FOR SYSTEM-ON-CHIP

Test data compression and test resource partitioning （TRP） are essential to reduce the amount of test data in system-on-chip testing. A novel variable-to-variable-length compression codes is designed as advanced fre- quency-directed run-length （AFDR） codes. Different [rom frequency-directed run-length （FDR） codes, AFDR encodes both 0- and 1-runs and uses the same codes to the equal length runs. It also modifies the codes for 00 and 11 to improve the compression performance. Experimental results for ISCAS 89 benchmark circuits show that AFDR codes achieve higher compression ratio than FDR and other compression codes.

An Efficient Test Data Compression Technique Based on Codes

This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method

System-on-Chip Test Data Compression Based on Split-Data Variable Length (SDV) Code

System-on-a-chips with intellectual property cores need a large volume of data for testing. The large volume of test data requires a large testing time and test data memory. Therefore new techniques are needed to optimize the test data volume, decrease the testing time, and conquer the ATE memory limitation for SOC designs. This paper presents a new compression method of testing for intellectual property core-based system-on-chip. The proposed method is based on new split- data variable length (SDV) codes that are designed using the split-options along with identification bits in a string of test data. This paper analyses the reduction of test data volume, testing time, run time, size of memory required in ATE and improvement of compression ratio. Experimental results for ISCAS 85 and ISCAS 89 Benchmark circuits show that SDV codes outperform other compression methods with the best compression ratio for test data compression. The decompression architecture for SDV codes is also presented for decoding the implementations of compressed bits. The proposed scheme shows that SDV codes are accessible to any of the variations in the input test data stream.

RESEARCH ON ADAPTIVE DATA COMPRESSION METHOD FOR TRIANGULATED SURFACES

NC code or STL file can be generated directly from measuring data in a fastreverse-engineering mode. Compressing the massive data from laser scanner is the key of the newmode. An adaptive compression method based on triangulated-surfaces model is put forward.Normal-vector angles between triangles are computed to find prime vertices for removal. Ring datastructure is adopted to save massive data effectively. It allows the efficient retrieval of allneighboring vertices and triangles of a given vertices. To avoid long and thin triangles, a newre-triangulation approach based on normalized minimum-vertex-distance is proposed, in which thevertex distance and interior angle of triangle are considered. Results indicate that the compressionmethod has high efficiency and can get reliable precision. The method can be applied in fastreverse engineering to acquire an optimal subset of the original massive data.

Design of quantum VQ iteration and quantum VQ encoding algorithm taking O（√N） steps for data compression

Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N） steps of distance computing between two vectors. The quantum VQ iteration and corresponding quantum VQ encoding algorithm that takes O（√N） steps are presented in this paper. The unitary operation of distance computing can be performed on a number of vectors simultaneously because the quantum state exists in a superposition of states. The quantum VQ iteration comprises three oracles, by contrast many quantum algorithms have only one oracle, such as Shor＇s factorization algorithm and Grover＇s algorithm. Entanglement state is generated and used, by contrast the state in Grover＇s algorithm is not an entanglement state. The quantum VQ iteration is a rotation over subspace, by contrast the Grover iteration is a rotation over global space. The quantum VQ iteration extends the Grover iteration to the more complex search that requires more oracles. The method of the quantum VQ iteration is universal.

Improved SDT Process Data Compression Algorithm

Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.

Design of real-time data compression wireless sensor network based on LZW algorithm

A real-time data compression wireless sensor network based on Lempel-Ziv-Welch encoding(LZW)algorithm is designed for the increasing data volume of terminal nodes when using ZigBee for long-distance wireless communication.The system consists of a terminal node,a router,a coordinator,and an upper computer.The terminal node is responsible for storing and sending the collected data after the LZW compression algorithm is compressed;The router is responsible for the relay of data in the wireless network;The coordinator is responsible for sending the received data to the upper computer.In terms of network function realization,the development and configuration of CC2530 chips on terminal nodes,router nodes,and coordinator nodes are completed using the Z-stack protocol stack,and the network is successfully organized.Through the final simulation analysis and test verification,the system realizes the wireless acquisition and storage of remote data,and reduces the network occupancy rate through the data compression,which has a certain practical value and application prospects.

DPCM-based vibration sensor data compression and its effect on structural system identification

Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.

RAPID CODEBOOK SEARCH ALGORITHM FOR SAR RAW DATA COMPRESSION

This paper presents a simple but eifective algorithm to speed up the codebook search in a vector quantization scheme of SAR raw data when a minimum square error(MSE) criterion is used. A considerable reduction in the number of operations is achieved.

ENHANCED MULTISTAGE VECTOR QUANTIZATION FOR SAR RAW DATA COMPRESSION

Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.

SIXTEEN TREE METHOD FOR BILEVEL IMAGE DATA COMPRESSION

A sixteen tree method of data compression of bilevel image is described.Thismethod has high efficiency,no information loss during compression,and easy to realize.

METHODS OF RADAR DATA COMPRESSION AND TARGET IDENTIFICATION BASED ON BIORTHOGONAL FDWT

In this paper, by using the biorthogonal quadrature filters, the biorthogonal mul-tiresolution analysis of finite dimension space equipped with inner product and the fast discrete wavelet transform (FDWT) are constructed. The dual transform method is proposed and the radar data storage is reduced by it. The method of choosing the wavelet coefficients, and the methods of correlation and nearest neighbor classification in wavelet domain based on the compressed data, are presented. The experimental results of the classification, using the high resolution range returns from six kinds of aircrafts, show that the methods of transform, compression and recognition are efficient.

A real-time algorithm for broadband highdynamic seismic data compression

A new real-time algorithm of data compression, including the segment-normalized logical compression and socalled 'one taken from two samples',is presented for broadband high dynamic seismic recordings. This algorithm was tested by numerical simulation and data observed. Its results demonstrate that total errors in recovery data are less than 1% of original data in time domain,0.5% in frequency domain, when using these two methods together.Its compression ratio is greater than 3.The data compression softwares based on the algorithm have been used in the GDS-1000 portable broadband digital seismograph.

A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

Covert channel of the packet ordering is a hot research topic.Encryption technology is not enough to protect the security of both sides of communication.Covert channel needs to hide the transmission data and protect content of communication.The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator.However,because the establishment of proxy communication needs to consume traffic,the communication capacity will be reduced,and in recent years,the tor technology often has vulnerabilities that led to the leakage of secret information.In this paper,the covert channel model of the packet ordering is applied into the distributed system,and a distributed covert channel of the packet ordering enhancement model based on data compression(DCCPOEDC)is proposed.The data compression algorithms are used to reduce the amount of data and transmission time.The distributed system and data compression algorithms can weaken the hidden statistical probability of information.Furthermore,they can enhance the unknowability of the data and weaken the time distribution characteristics of the data packets.This paper selected a compression algorithm suitable for DCCPOEDC and analyzed DCCPOEDC from anonymity,transmission efficiency,and transmission performance.According to the analysis results,it can be seen that DCCPOEDC optimizes the covert channel of the packet ordering,which saves the transmission time and improves the concealment compared with the original covert channel.

A Complexity Analysis and Entropy for Different Data Compression Algorithms on Text Files

In this paper, we analyze the complexity and entropy of different methods of data compression algorithms: LZW, Huffman, Fixed-length code (FLC), and Huffman after using Fixed-length code (HFLC). We test those algorithms on different files of different sizes and then conclude that: LZW is the best one in all compression scales that we tested especially on the large files, then Huffman, HFLC, and FLC, respectively. Data compression still is an important topic for research these days, and has many applications and uses needed. Therefore, we suggest continuing searching in this field and trying to combine two techniques in order to reach a best one, or use another source mapping (Hamming) like embedding a linear array into a Hypercube with other good techniques like Huffman and trying to reach good results.

Sea Route Monitoring System Using Wireless Sensor Network Based on the Data Compression Algorithm

The wireless sensor network （WSN） plays an important role in monitoring the environment near the harbor in order to make the ships nearby out of dangers and to optimize the utilization of limited sea routes. Based on the historical data collected by the buoys with sensing capacities, a novel data compression algorithm called adaptive time piecewise constant vector quantization （ATPCVQ） is proposed to utilize the principal components. The proposed system is capable of lowering the budget of wireless communication and enhancing the lifetime of sensor nodes subject to the constrain of data precision. Furthermore, the proposed algorithm is verified by using the practical data in Qinhuangdao Port of China.

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.

AN EFFICIENT ECG DATA COMPRESSION METHOD

-An efficient ECG (Electrocardiogram) data compression algorithm called KPDEC (key point detection and error compensation) is presented in this pa-Per. With tkis KPDEC method only the key points (KPs) of ECG signals are con-sidered to be saved to make the compression more efficient. These KPs can be ex-tracted from ECG samples by calculating the second-ordered central difrerences.Then an error pre-correcting technique is used to let the saved sample having a rea-sonable compensation berore it is stored. This technique is able to reduce the PRD (Percentage Root Mean Square Difference) obviously. In the paper we describe an optimal cording sckeme for getting higer compression rate. Furthermore, an adap-tive filtering tecknique is designed for reconstructed ECG signals to get better fi-delity waves. The algorithm is able to compress ECG data to 168 bits per second with PRD less than 3%.

Curvature Quantified Douglas-Peucker-based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.