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In big data environment, data loss is a crucial issue which probably will occur due to the high network traffic, transmission delay and lesser bandwidth. This problem could be solved by adopting data compression schemes. These schemes could be classified into two types based on their actions: lossless compression and lossy compression. Lossy compression changes the output which will not be the same as input. Lossless compression changes the output and produces the output same as the input data. So the network overhead could be increased. The existing fixed and variable length coding technique have high robustness but poor efficiency. The efficiency problem can be solved by using the proposed scheme called “Data compression and parallel computation research model”. This proposed model uses a more sophisticated coding technique for the data compression and increases the efficiency while reducing the delay. Simulation results have shown that the proposed data compression and parallel computation research model has the better signal to noise ratio, increases the efficiency and reduces the delay when comparing to the existing models.

Chemical vapor deposition (CVD) of copper and manganese can produce interconnects scaled down to below 10 nm, while enhancing their conductivity and lifetime. CVD using similar super-conformal processes can enable very narrow through-silicon-vias, as well as tiny and robust flexible wires between chips. Silica insulating layers can be made by a super-conformal and rapid atomic layer deposition (ALD) process.

An arbitrary sampling rate conversion algorithm based on frequency domain filtering (FDF-ASRC) is proposed. The number of Fourier coefficients in FDF-ASRC is irrelevant to the symbol rate of interested signal. Applying the resampling algorithm to non-uniform channelization, a few modules of Fourier transform (FT) and inverse Fourier transform (IFT) can guarantee infinite channels in channelization. Meanwhile, the sampling rate of output signal can be adjusted to an integer multiple of symbol rate, which can be passed to demodulator directly. By comparison with seven architectures of non-uniform channelizers, the computational complexity of the proposed channelizers is equal to the best.

The multi-phase fuzzy evaluation method is applied to grade operator in dynamic process in power plant. This method simulates evaluation mode of instructor to some extent. The process of drum level variation is divided into three phases. There are different fuzzy demands for level control in each phase. Fuzzy evaluation method is used for evaluation factors in single phase. Then the comprehensive evaluation result is computed with the weighted average method of each factor in each phase. Simulation results show that the grade calculated by fuzzy evaluation method is closed to that by instructor. The grade for operator's operation is usually given by instructor subjectively. The method could evaluate operator objectively and take the place of instructor in grading operator.

To diagnose a slight or incipient fault in a thermodynamic system correctly and timely, a new fault recognition approach is put forward by joint application of fault symptom zoom technology (SZT) and fuzzy pattern recognition. By studying the rules of the thermal system faults related to energy and mass balance, a fault symptom preprocessing method, which is called "fault symptom zoom technology", is put forward to preprocess the fault characteristic parameters, with which the complexity of the thermal system fault knowledge library can be effectively reduced and the slight fault recognition ability can be greatly enhanced. The fault fuzzy pattern recognition method is introduced, and a new general-purpose fuzzy recognition function is given, which fits for various kinds of fault symptoms and is with favorable fault classifying ability. Some fault diagnostic examples for incipient and slight faults of the thermodynamic system are given, which verify the effectiveness of the method.