A multi band integrated antenna scheme for the fifth generation (5G) mobile phone is proposed, that is, one antenna integrates five bands, namely Global Positioning System (GPS) Level 1 (L1), GPS Level 5 (L5), Wi-Fi 2.4G, Wi-Fi 5G and Long Term Evolution (LTE) band 32, the antenna volume for the whole mobile phone is effectively saved. This five-in-one antenna is located in the upper left corner of the back of the mobile phone, the upper clearance and side clearance of mobile phone are 1mm and 0.2mm respectively. The size of five-in-one antenna is 38.5mm by 26mm. The form of five-in-one antenna is combining inverted F antenna (IFA) and parasitism. This antenna is radiated by metal frame and laser direct structring (LDS), measurement results show that, passive efficiency in free space of GPS L1, GPS L5, Wi-Fi 2.4G, Wi-Fi 5G and LTE Band 32 could reach -4.5dB, -8.5dB, -6dB, -4.5dB, -6.5dB respectively, also the specific absorption rate (SAR) value of the transmission band is lower than Federal Communications Commission (FCC) specification. A mobile phone equipped with this five-in-one antenna has been commercially shipped.
A multi-channel (9*X) data acquisition system based on FPGA and MCU is developed, which can be used for signal field strength detection of base station equipment such as RFID. The system consists of data acquisition array, central controller, embedded firmware, and upper computer. Using the modular design method, the data acquisition array can be designed by changing the number of channels. The single-channel data acquisition module has a sampling resolution of 16 bits, a sampling rate of 200 kSPS/ch and dynamic range 70dB. The master-slave architecture is used to realize multi-channel synchronous acquisition of data. When the central controller communicates with the upper computer, it controls the operation of the single-channel data acquisition module and obtains the sampling data in a parallel manner. The embedded firmware is written in Verilog and C language to realize data acquisition, buffering, and sending and receiving of uplink and downlink signaling. The upper computer has the functions of system control and communication, establishes a data link with the central controller through the Ethernet cable, and obtains multi-channel sampling data through signaling. The system verifies the performance of the multi-channel data acquisition system through Analog-to-digital converter, single-channel data acquisition module and system performance test.
Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images.
Traditional ad-hoc is infrastructure-less. UEs (User Equipment) can realize rapid networking, but cannot guarantee the reliability of D2D (Device to Device) connection. 5G provides a network infrastructure of reliable connection, based on this, D2D provides visualized connection i.e. an autonomously controllable "virtualization ad-hoc". In order to integration of 5G D2D (ie. DCE) with DTE (Data terminal equipment) in power grids’ automation control and protection in the field area, dual-plane redundancy in substation and heterogeneous hand in hand connection in distribution power lines are proposed. Cross-layer connection failure detection and autonomous maintenance are further discussed. The applicable test shows the method can significantly improve the reliability of the power grids’ D2D network.
Nowadays, the rapid development of Internet technology drives the advance of the big data era, and along with the widespread use of smart devices and social media, multimedia data is growing explosively. With the increasingly complex and diverse needs of information exchange, collection and storage, the types of information have also evolved from traditional text information to diverse data forms such as pictures and video and audio, bringing different degrees of convenience to people's work and life and other scenarios. However, the huge amount of multimedia data also makes information storage and retrieval more cumbersome. How to realize the effective storage and efficient retrieval of data, so as to better utilize the value of multimedia data, is one of the challenges that academia and information industry are tackling nowadays. In this paper, we study the cross-media retrieval technology of text and image by Contrastive Language-Image Pre-training model based on natural language processing method. The cross-media pre-training idea proposed in this paper can be applied not only to text-image processing, but also theoretically to mutual retrieval of modal information of video and audio, etc.
