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A 90–96 GHz down-conversion mixer for 94 GHz image radar sensors using standard 90 nm CMOS technology is reported. RF negative resistance compensation (NRC) technique, i.e. NMOS LC-oscillator-based RF transconductance (GM) stage load, is used to increase the output impedance and suppress the feedback capacitance Cgd of RF GM stage. Hence, conversion gain (CG), noise figure (NF) and LO-RF isolation of the mixer can be enhanced. The mixer consumes 15 mW and achieves excellent RF-port input reflection coefficient of −10∼−36.4 dB for frequencies of 85∼105 GHz. The corresponding −10 dB input matching bandwidth is 20 GHz. In addition, for frequencies of 90∼96 GHz, the mixer achieves CG of 6.3∼9 dB (the corresponding 3-dB CG bandwidth is greater than 6 GHz) and LO-RF isolation of 40∼45.1 dB, one of the best CG and LO-RF isolation results ever reported for a down-conversion mixer with operation frequency around 94 GHz. Furthermore, the mixer achieves an excellent input third-order intercept point (IIP3) of 1 dBm at 94 GHz. These results demonstrate the proposed down-conversion mixer architecture is very promising for 94 GHz image radar sensors.

With the advancement of technology, the traditional e-learning model may expand the realm of knowledge and differentiate learning by means of deep learning (DL) and augmented reality (AR) scenarios. These scenarios make use of interactive interfaces that incorporate various operating methods, angles, perceptions, and experiences, and also draw on multimedia content and active interactive models. Modern education emphasizes that learning should occur in the process of constructing knowledge scenarios and should proceed through learning scenarios and activities. Compared to traditional “spoon-feeding” education, the model learning scenario is initiated with the learner at the center, allowing the person involved in the learning activity to solve problems and further develop their individual capabilities through exploring, thinking and a series of interactions and feedback. This study examined how students in the agriculture technological industry make use of AR digital learning to develop their industry-related knowledge and techniques to become stronger and more mature so that they unconsciously apply these techniques as employees, as well as encouraging innovative thought and methods to create new value for the enterprise.

This paper presents a cascode low-noise amplifier (LNA) with shunt inductors and transformer feedback $\mathbf{g}_{\mathbf{m}^{-}}$ boosting technique. In the cascode, a shunt inductor is added between the common source and the common gate stage for impedance matching. In the common gate stage, a transformer is designed to increase the transconductance and resonate with the parasitic capacitance of the transistors. Both techniques boost the operation frequency, gain, and noise figure (NF). Fabricated in a 90-nm CMOS process, the proposed LNA exhibits a minimum NF of 11.9 dB, a maximum gain of 6.6 dB, and it consumes only 10.4 mA current from a 1.2-V supply.

An ultra low phase noise millimeter-wave (mm-wave) quadrature voltage-controlled oscillator (QVCO) is proposed. By introducing an additional coupling path and using uneven sized cross-coupled oscillators with a fully symmetrical layout, the phase noise and the amplitude/phase mismatches are minimized. Implemented in 90-nm CMOS process, the 67-GHz compact QVCO achieves 0.46 degree and 0.47 dB phase and amplitude mismatches, respectively, and a phase noise of −105.8 dBc/Hz at 1-MHz offset, resulting in a figure-of-merit of −183 dBc/Hz.

Based on the theoretical analysis of solar cell,a kind of photovoltaic cell simulator’s main circuit and relevant control system are designed.What’s more,the simulation model is build up under the solar cell array simulator by Simulink.We also carry out researches as well as simulation analysis with regard to its working conditions with resistance loaded. In the end , the analysis of the simulation’s outcome confirms the correctness of our designed circuit and model .

The optimal investment strategies of defined-contribution pension under the quadratic utility function are studied in the paper. In our model, the plan member is allowed to invest in a risk-free asset and a risky asset, which is described by a constant elasticity of variance (CEV) model. By applying Legendre transform and dual theory, the non-linear second partial differential equation is transformed into a linear partial differential equation in order that the explicit solution for the quadratic utility function is found. The result shows that the optimal proportion invested in risky assets for the pension investor with the exponential utility function is divided into three parts: moving Merton factor, correction factor, contributions' factor and the correction factor is a monotone decreasing function with respect to time t.