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Colleges and universities are the most informatized places in our society. With the widespread application of computer and network technology, the Internet has become an important channel for college students to acquire knowledge and various information, and is important for college students’ study, life, and ideas. Both have a broad and profound impact. College ideological and political education is bound to face how to respond to the challenges and opportunities brought about by new technologies. This article examines the network ideological and political education system in China’s colleges and universities from a new perspective. This paper discusses the construction of a network platform for ideological and political education of college students, and opens up new carriers and new channels for teacher-student communication and communication. The platform breaks the time and space limitations of the traditional ideological and political education model, improves teaching efficiency and quality, and is very useful for realizing true quality education and flexible and individualized education, and training qualified socialist builders and successors. Significance.

Education is moving towards the era of big data. Nowadays, the value of data and data analysis can be reflected everywhere in college ideological and political education. The processing process of ideological and political education big data can be basically divided into four stages: data perception and collection, data processing and integration, data analysis and data interpretation. With the help of web log analysis, social network analysis, and correlation analysis, the ideological and political education big data Analysis can make the decision-making process of ideological and political education more scientific, and the measures formulated are more targeted and more in line with educational needs. Of course, information security and the difficulty of data acquisition also bring challenges to the application of big data in ideological and political education.

Mass segmentation in mammography images is one of the effective ways to screen breast cancer. The accurate segmentation of the pectoral muscle can improve the accuracy of mass recognition. However, the results of traditional mammography image segmentation methods often appear incomplete segmentation and over-segmentation, the accuracy is low, which directly affects the accuracy of breast cancer screening. To solve these problems, a segmentation method of mammography images based on spectral clustering is proposed in this paper. Firstly, we use the spectral clustering to segment the pectoral muscle preliminarily. In view of the stratification of pectoral muscle and the unclear boundary of breast muscle and breast tissue, we use the maximum grayscale difference constraint and shape constraint to achieve accurate breast muscle segmentation. The mass is recognized accurately with the segmented image. The experimental results of the MIAS breast image database show that the proposed method can effectively segment the uneven grayscale pectoral muscle caused by the overlap of the pectoral muscle tissues, and it is robust to the segmentation of tumors of different sizes.

Location is one of the basic services provided by the Wireless Sensor Networks (WSN), which is not only a requirement for geography location information, but also a decisive role in network routing, topology configuration, and load balancing. In order to balance the positioning accuracy and the positioning response time, particle swarm optimization (PSO) algorithm is introduced in the process of WSN localization. Firstly, the parameters of PSO are set and the mathematical model of WSN localization is established. Then, the hybrid mechanism is adopted, and the crossover and mutation factors are used to make PSO algorithm have the ability to jump out of the local optimal solution and find a better solution. Finally, the simulation results show that the method can not only improve the location accuracy, but also reduce the location response time.

Arrayed waveguide grating (AWG) has become a key component in commercial wavelength division multiplexing (WDM) systems for its multichannel wavelength selective function [1]. A 4×4 arrayed waveguide grating (AWG) demultiplexer is designed and fabricated based on silicon on insulator (SOI) nanowire. All of the device design is performed based on the SOI strip nanowire waveguide with SiO 2 upper-cladding. SOI wafer with a top silicon layer of 220 nm-thickness and a buried oxide (BOX) layer of 2 µm thickness. The 4×4 arrayed waveguide grating (AWG) with a channel spacing of 3.2 nm, the waveguide has a height of 220nm and a width of 450 nm, which ensures single-mode transmission and a low propagation loss. The waveguide sidewall roughness causes large phase distortion of the arrayed waveguides, deteriorating insertion loss and crosstalk of AWG. By broadening the straight waveguide width to 800 nm, it is obviously suppressed insertion loss and crosstalk. 30 arrayed waveguides with constant length difference are located between two FPRs. The device is fabricated on a SOI substrate mainly by electron beam lithography (EBL) and inductively coupled plasma (ICP) etching. The demultiplexing characteristics are observed with a loss of about 9.6 dB, a channel spacing of 2.8–3.6nm and crosstalk of about 4 dB. The 3 dB bandwidth is about 5 nm. Figure 1 show the Measured transmission spectra of fabricated AWG.

This paper presents experimental results of an analog baseband circuit for China multimedia mobile broadcasting (CMMB) direct conversion receiver in 0.35 um SiGe BiCMOS process. It is the first baseband of CMMB RFIC reported so far. A 8th-order chebyshev low pass filter (LPF) with calibration system is used in the analog baseband circuit, the filter provides 0.5 dB passband ripple and -35 dB attenuation at 6 MHz with the cutoff frequency at 4 MHz, the calibration of filter is reported to achieve the bandwidth accuracy of 3%. The baseband variable gain amplifier (VGA) achieves more than 40 dB gain tuning with temperature compensation. In addition, A DC offset cancellation circuit is also introduced to remove the offset from layout and self-mixing, and the remaining offset voltage and current consumption are only 6 mV and 412 uA respectively. Implemented in a 0.35 um SiGe technology with 1.1 mm2 die size, this tuner baseband achieves OIP3 of 25.5 dBm and dissipate 16.4 mA under 2.8-V supply.

In this paper, a charge-pump based phase-locked loop (CPLL) that can achieve fast locking and tiny deviation is proposed and analyzed. A lock-aid circuit is added to achieve fast locking of the CPLL. Besides, a novel differential charge pump which has good current matching characteristics and a PFD with delay cell has been used in this PLL. The proposed PLL circuit is designed based on the 0.35 um 2P4M CMOS process with 3.3 V/5 V supply voltage. HSPICE simulation shows that the lock time of the proposed CPLL can be reduced by over 72% in comparison to the conventional PLL and its charge pump sink and source current mismatch is only 0.008%.

A seven-state phase frequency detector (S.S PFD) is proposed for fast-locking charge pump based phase-locked loops (CPPLLs) in this paper. The locking time of the PLL can be significantly reduced by using the seven-state PFD to inject more current into the loop filter. In this stage, the bandwidth of the PLL is increased or decreased to track the phase difference of the reference signal and the feedback signal. The proposed architecture is realized in a standard 0.35 ?m 2P4 M CMOS process with a 3.3 V supply voltage. The locking time of the proposed PLL is 1.102 ?s compared with the 2.347 ?s of the PLL based on continuous-time PFD and the 3.298 ?s of the PLL based on the pass-transistor tri-state PFD. There are 53.05% and 66.59% reductions of the locking time. The simulation results and the comparison with other PLLs demonstrate that the proposed seven-state PFD is effective to reduce locking time.