This paper presents a 140 GHz IF beamforming phased-array receive channel with low noise performance in 45nm RFSOI process. The proposed high-IF (9-14 GHz) beamforming architecture realizes the lower loss and power consumption compared with RF and LO beamforming at 140 GHz, while rejecting the noise contribution from the image without external filtering. In this design, a fully-differential low-noise amplifier (LNA) is followed by an active double-balanced mixer and an IF beamformer, consisting of a variable gain amplifier (VGA) and a vector modulator (VM). A x6 on-chip multiplier is used to generate the 126-138 GHz local oscillator. The receiver consumes 133 mW with a measured peak gain and average NF of 26.5 dB and 7 dB, respectively (NF 6.4-7.5 dB at 134-149 GHz). The measured input P1dB is -30+/-1 dBm at 139-142 GHz. To our knowledge, this work presents the first phased-array beamformer receive channel with the lowest NF in the 140 GHz band.
We describe a custom wireless power and data transmission (WPDT) link and analyze its performance in a prototype implantable sensor system of ensembles of CMOS sensor ASICs ("Neurograins") embedding 0.5 mm × 0.5 mm planar microcoil antennas. We use near-field RF at ~1 GHz for wireless powering in a resonant 3-coil architecture including an implanted relay coil in a quadrant layout architecture to maximize coverage area and RF transfer efficiency. We demonstrate successful WPDT across antenna cross-section in benchtop proxy physiological tests. Demodulation and analysis of backscattered signals validate the data link fidelity. Our results suggest that this electromagnetic coupling scheme can robustly support a chip density of 250/ cm 2 (up to 1024 individual Neurograins in a 2 cm × 2 cm area) and parallel transmitters can be combined to multiply the channel capacity without destructive interference.
In the power system with a high proportion of new energy access, the original electric vehicle charging operation mode of the distribution network will change, and the acceptance capacity of electric vehicles also needs to be re-evaluated. In this paper, an evaluation method for the acceptance capacity of electric vehicles is proposed for the distribution network with photovoltaic power generation system installed. Considering the random variation characteristics of the output of the photovoltaic power generation system, seasonal periodicity, and the influence of weather factors, an evaluation model is established. It uses the particle swarm optimization algorithm to calculate the number of electric vehicles that the photovoltaic power generation system can accept, and to analyze the acceptance capacity of the distribution network for electric vehicles. The method utilizes the photovoltaic output to fill the electric vehicle charging demand, and can realize the mutual consumption of the electric vehicle charging and the photovoltaic output.
The fusion between the low resolution hyperspectral image (LRHSI) and the panchromatic (PAN) image could obtain the high-resolution hyperspectral image (HRHSI). Recently, deep learning (DL)-based fusion methods have been explored widely due to their powerful feature learning ability. However, most DL-based methods that use the one-step fusion manner can suffer from the blurring effect. In addition, they have not fully utilized the spatial and spectral feature information of two input images, which hinders the improvement of the resulting image quality. Therefore, to fully mitigate the blurring effect and utilize two input images, we propose a dual conditional diffusion models-based fusion network (DCDMF) to obtain the fused HRHSI. The conditional diffusion model (CDM) can generate the high quality image with realistic details in an iterative denoising manner (in the inference sampling stage) other than the one-step fusion manner, which could mitigate the blurring effect greatly. To improve the spatial and spectral fidelity of the fused HRHSI, we propose the dual spatial and spectral CDM (two noise prediction networks with different conditional input) to respectively extract the image feature from the LRHSI and PAN images with different image characteristics and reconstruct the corresponding HRHSI feature and fuse them. In addition, considering the high-dimensional property of the HSI, we pre-train an auto-encoder to encode the HSI into the low-dimensional latent space with more discriminate features to reduce the computational cost. Based on the auto-encoder, we also perform the image generation process in the residual latent space to focus on learning the residual latent spatial details. Extensive experimental results on three datasets show the superiority of our method over several state-of-the-art (SOTA) methods. (The ziyuan dataset and codes could be available at https://github.com/rs-lsl/DCDMF).
A numerical simulation was built to investigate recirculatory filtration fume hood,with the constant face velocity,changing the concentration of pollutant filtrated by the fume hood exit filter to analyze the velocity field and concentration distribution in the room.In addition,general ventilation and different air changes per hour's influence on the velocity field and concentration distribution is also simulated.
MC－CDMA，acronym of multicarrier code division multiple access，is actually the result of the application of the OFDM system in the DS－CDMA system，a sort of hybrid technology that combines the advantages of both DS－CDMA and OFDM．This paper analyses the reasons why OFDM is applied in the DS－CDMA system，introduces a model of the MC－CDMA system and explains MC－CDMA＇s advantage in resisting frequency fading．
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