The degree of achievable test-data compression depends on not only the compression scheme but also the structure of the applied test data. Therefore, it is possible to improve the compression rate of a given test set by carefully organizing the way that test data are present in the scan structure. The relationship between signal probability and test-data entropy is explored in this paper, and the results show that the theoretical maximum compression can be increased through a partition of scan flip-flops such that the test data present in each partition have a skewed signal distribution. In essence, this approach simply puts similar scan flip-flops in an adjacent part of a scan chain, which also helps to reduce shift power in the scan test process. Furthermore, it is shown that the intrapartition scan-chain order has little impact on the compressibility of a test set; thus, it is easy to achieve higher test compression with low routing overhead. Experimental results show that the proposed partition method can raise the compression rates of various compression schemes by more than 17%, and the average reduction in shift power is about 50%. In contrast, the increase in routing length is limited.

Code-based test data compression schemes encode symbols in the test data with predetermined codewords so that data volume can be reduced. The compression efficiency is affected by the distribution of data symbols. In this paper, we first analyze the factors that affect the encoding efficiency in various codes, and then propose a skewed-distribution scan chain partitioning scheme, in which the distribution of 0’s and 1’s are changed in different parts of the scan chain. Both analytical and experimental results confirm that the scheme can effectively improve compression efficiency, while the routing penalty due to the partitioning method is limited.

The mobile location in wireless communication system becomes a popular issue in recent years. It is commonly used in emergency services, roadside assistance, billing, navigation, psychophysics, behavioral science, etc. Over recent decades, many location algorithms are put forward. However, the algorithms results in inaccurate location estimation due to Non-Line-Of-Sight (NLOS) propagation. This paper proposes the mobile location estimation based on the Received Signal Strength (RSS) and the Time-Of-Arrival (TOA) measurements, via the multidimensional scaling technique (MDS). The algorithm does not require a known and accurate path loss modeling, reduces the impact of NLOS on location. Simulation results show that the algorithm performs well even though in the severe NLOS scenario.