In wireless communication systems, the conventional constant modulus algorithm incurs artificial error and steady state misadjustment. In this study, an improved constant modulus algorithm (ICMA) and its generalized form are proposed for blind equalization. The ICMA utilizes the clustering function of Gaussian function to efficiently suppress this artificial error and steady state misadjustment at the cost of reduction in the sample usage rate. Moreover, the generalized form of the ICMA is developed to ensure the sample usage rate while the good performances of the ICMA are maintained. Simulation results illustrate better equalization performances of the ICMA and generalized ICMA, as compared to the classical constant modulus algorithm.

Log data is an essential and valuable resource of online service systems, which records detailed information of system running status and user behavior. Log anomaly detection is vital for service reliability engineering, which has been extensively studied. However, we find that existing approaches suffer from several limitations when deploying them into practice, including 1) inability to deal with various logs and complex log abnormal patterns; 2) poor interpretability; 3) lack of domain knowledge. To help understand these practical challenges and investigate the practical performance of existing work quantitatively, we conduct the first empirical study and an experimental study based on large-scale real-world data. We find that logs with rich information indeed exhibit diverse abnormal patterns (e.g., keywords, template count, template sequence, variable value, and variable distribution). However, existing approaches fail to tackle such complex abnormal patterns, producing unsatisfactory performance. Motivated by obtained findings, we propose a generic log anomaly detection system named LogAD based on ensemble learning, which integrates multiple anomaly detection approaches and domain knowledge, so as to handle complex situations in practice. About the effectiveness of LogAD, the average F1-score achieves 0.83, outperforming all baselines. Besides, we also share some success cases and lessons learned during our study. To our best knowledge, we are the first to investigate practical log anomaly detection in the real world deeply. Our work is helpful for practitioners and researchers to apply log anomaly detection to practice to enhance service reliability.

This paper proposes a novel hierarchical approach to the efficient analysis of large VLSI power/ground grids. Different from the existing hierarchical approach where sub-circuit equivalent models are sparsified with computation-intensive integer programming and the resulting modeling may lead to larger errors if the top circuit matrix has large condition number, the new approach employs an iterative (relaxation) procedure to explicitly compensate the errors and avoid introducing dense matrix caused by the circuit reduction. We also propose an efficient scheme for partitioning high performance center-bumped P/G grids. Experimental results demonstrate that the new algorithm is more accurate than the existing hierarchical method while delivering more speedup over the flat simulators.

In today's power/ground(P/G) network design, on-chip decoupling capacitors(decaps) are usually made of MOS transistors with source and drain connected together. The gate leakage current becomes worse as the gate oxide layer thickness continues to shrink below 20Å. As a result, decaps will become leaky due to the gate leakage from CMOS devices. In this paper, we take a first look at the leaky decaps in P/G network optimization. We propose a leakage current model for practical decaps and also present a new two-stage leakage-current-aware approach to efficiently optimize P/G networks in a more area efficient way.