Concerning the limitations of Spark, including immutable datasets and significant costs of code execution, memory management and data serialization/deserialization caused by running environment of Java Virtual Machine (JVM), a light-weight big data processing system, named Helius, was implemented in C/C++. Helius supports the basic operations of Spark, while allowing the data set to be modified as a whole. In Helius, the C/C++ is utilized to optimize the memory management and network communication, and a stateless worker mechanism is utilized to simplify the fault tolerance and recovery process of the distributed computing platform. The experimental results showed that in 5 iterations, the running time in Helius was only 25.12% to 53.14% of that in Spark when running PageRank iterative jobs, and the running time in Helius was only 57.37% of that in Spark when processing TPCH Q6. On the basis of one iteration of PageRank, the IP incoming and outcoming data sizes of master node in Helius were about 40% and 15% of those in Sparks, and the total memory consumed in the worker node in Helius was only 25% of that in Spark.Compared with Spark, Helius has the advantages of saving memory, eliminating the need for serialization and deserialization, reducing network interaction and simplifying fault tolerance.

To enhance the noise restraint of modulated signal carrier frequency estimation algorithm based on linear fitting, in accordance with the features of modulating signal in the time domain, an estimation algorithm combined with RANdom Sample Consensus (RANSAC) based on least square method was proposed. It regarded modulating signals obtained by Amplitude-Shift Keying (ASK) and Phase-Shift Keying (PSK) which have single carrier frequency as research subjects, and did simulation experiments in Matlab. The experimental results indicate that the method introduced in this paper is better than the method based on least square method in the error rate and noise restraint.