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Reconfigurable Intelligent Surface (RIS) can improve the security of the physical layer of wireless communication by adjusting the phase of the reflective unit. After analyzing the common theoretical models and potential problems, this study proposes an alternative iterative model based on multiple input multiple output (MIMO), and designs the transmitter, channel and receiver. In addition, passive beamforming precoding matrix of RIS was jointly optimized, and the Lagrangian dual relaxation (LDR) was adopted to decouple the nonconvex problem. After that, the active and passive beamforming matrices were subjected to iterative calculation, and the beamforming was optimized through cyclic programming at the base station (BS). In addition, the high convergence of the proposed algorithm was proved by MATLAB simulation. The results of simulation demonstrate that the joint precoding framework algorithm can maximize the weighted sum rate (WSR), which in turn demonstrates the feasibility of our method. Finally, the authors analyzed the strong applicability of the RIS technology in complex wireless networks with different volatility, revealing the possibility of future development.

This paper designs a deep learning-based closed-loop detection algorithm for indoor space. You only look once (YOLO) v3 was adopted to detect the objects in the scene, extract the semantic and position information of the non-dynamic objects contained in the current frame, and solve the similarities between the current frame and key historical frame, thereby completing closed-loop detection. In our network structure, the prior static semantic library is employed to differentiate and eliminate the dynamic objects in the scene, such that the network can apply to most indoor scenes. In addition, the closed-loop detection was made immune to the disturbance of dynamic objects. The extracted semantic information can be applied to modules like visual odometer and semantic maps.

Infectious cloning of plant viruses is a powerful tool for studying the reverse genetic manipulation of viral genes in virus–host plant interactions, contributing to a deeper understanding of the life history and pathogenesis of viruses. Yet, most of the infectious clones of RNA virus constructed in E. coli are unstable and toxic. Therefore, we modified the binary vector pCass4-Rz and constructed the ternary shuttle vector pCA4Y. The pCA4Y vector has a higher copy number in the E. coli than the conventional pCB301 vector, can obtain a high concentration of plasmid, and is economical and practical, so it is suitable for the construction of plant virus infectious clones in basic laboratories. The constructed vector can be directly extracted from yeast and transformed into Agrobacterium tumefaciens to avoid toxicity in E. coli. Taking advantage of the pCA4Y vector, we established a detailed large and multiple DNA HR-based cloning method in yeast using endogenous recombinase. We successfully constructed the Agrobacterium-based infectious cDNA clone of ReMV. This study provides a new choice for the construction of infectious viral clones.

With the development of mobile communications and Internet of things (IoT), the Internet of vehicles (IoV) has become commonplace. To integrate the heterogenous IoV networks, this paper mainly explores the heterogenous network selection mechanism for the IoV. Considering the diversity of IoV networks, the authors proposed a heterogenous network selection algorithm based on comprehensive weight. Based on different service scenarios and user preferences, the proposed algorithm obtains the subjective weights of candidate networks, calculates the objective weights of judgement indices by entropy method, and combines the subjective and objective weights into a comprehensive weight. On this basis, the candidate networks were analyzed by the utility function (UF), and the optimal network was selected for access. The research results provide a reference for high-quality access to heterogenous networks.

Rehmannia glutinosa is a top-grade traditional Chinese medicine, and also is an important planting medicinal material for Chinese poor farmers shaking off poverty. Rehmannia mosaic virus (ReMV) causes big economic loss of R. glutinosa in planting area. However, there is no effective methods for quick, accurate, and high-throughput detection for ReMV in Chinese production area. The preserved R. glutinosa samples carrying ReMV was taken for research material. The coat protein coding sequences (CPReMV) was cloned and sequenced. The target sequence was further placed into a prokaryotic expression vector to express the N-terminal-tagged recombinant CPReMV protein (His-CPReMV). Purified His-CPReMV was used as an antigen to immunize New Zealand white rabbits, and antiserum was obtained. The titers and sensitivities of the antisera were analyzed and evaluated. Polyclonal antibodies were purified from the antiserum, and the titers and sensitivity to the target His-CPReMV protein were evaluated. The results demonstrate that the obtained polyclonal antibodies against His-CPReMV could be successfully used for rapid, accurate, and high-throughput detection of ReMV from R. glutinosa planted in the wild. Our investigation established serological-based detection methods for ReMV for the first time, and provides a foundation for future exploration of the pathogenic mechanisms of ReMV in R. glutinosa.

This design was aimed at the requirements of the asset tracker's working time, abnormal identification, remote alarm, information prompt, using STM32 as the core MCU for data collection and processing, with a built-in GNSS wireless communication module, three-axis acceleration sensor, and other sensors, design and implement an asset tracker device that automatically recognizes and reports abnormalities. In this design, the GPS positioning information was processed, and the positioning accuracy of the device was improved. The acceleration sensor data was performed by the Kalman filter, which could effectively judge the movement of assets. The sleep-work-sleep work mode was adopted to reduce the device's power consumption and enhance the device's endurance. The test results showed that the device could reasonably identify the device's abnormal condition, quickly locate the device, and upload the device information to the server. Each working life could be applied to the tracking of all kinds of assets.

Nowadays air pollution is serious. A humidifier can effectively improve the ambient humidity, and the negative oxygen ions released by its atomization are beneficial to people’s health. However, the function of the humidifier is relatively simple at present. An STM32 is used as the core processing unit, a speech recognition module based on LD3320 and a speech synthesis module based on SYN6288 are chosen as the control mode, a more practical and convenient smart humidifier is designed in this paper. ADHT11 temperature and humidity sensor are used to collect ambient humidity, and the collected information is displayed in OLED after MCU processing. If the current air humidity is less than the set value and the water level reaches the standard, the humidifier will begin to humidify. Voice warning is issued when the water level of the humidifier is too low. Besides, the opening and stopping of humidification can be controlled by voice. The testing results show that the humidifier designed is stable with a high-speech recognition rate.

With the rapid development of modern electronic technology and the continuous improvement of the performance of optoelectronic devices, laser rangefinder has become the main instrument for distance measurement and is soon applied to various measurement fields. In this paper, a laser rangefinder based on a micro-programmed control unit (MCU) is designed according to the principle of laser pulse range finding. The proposed design uses an MCU based on Corex-M4 core as the processor, uses a range-finding module to obtain the single-point distance signal, and combines with a gyroscope sensor to measure the distance between two points on a plane. After data collected is processed by the MCU, the distance information is displayed on an OLED screen. At the same time, this information can be also transmitted to the upper computer through Bluetooth for storage and subsequent processing. The test results show that the proposed design has the advantages of fast measurement, high accuracy, and reliability, which can meet the needs of range-finding in people's daily work and life.

With the rapid development of social economy, excessive discharge of industrial sewage leads to water pollution, and it also affects the quality of domestic water. Therefore, it is necessary to monitor the water quality. A practical and convenient water quality monitoring system is designed in this paper, which is based on the MCU (Micro-programmed Control Unit) and Bluetooth technology. This design takes the Arduino development board based on ATMega328P chip as the core, and uses sensors to collect pH, turbidity, conductivity and water temperature. The measured data is sent to the smart phone via Bluetooth, and abnormal parameters are prompted. The test results show that the system can obtain the water quality parameters in time and accurately, and the overall operation of the system is stable, which is suitable for many occasions of water quality monitoring.

Identifying patients with mild cognitive impairment (MCI), who are at high risk of progressing to Alzheimer’s disease (AD), is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine- (ELM-) based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance images (MRI) and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion.

With the rapid increase of global data traffic, storage and transmission of massive data is a challenging problem. Therefore, data compression is necessary. Based on DRoF, mobile fronthual has the advantages of high data rate, good connectivity and system capacity, and becomes the key part of communication system transmission. In this paper, an optimized vector quantization data compression algorithm based on DRoF system is proposed, which mainly studies OFDM baseband modulation signals. Firstly, the modulation signal is transformed by DCT. Then the optimized fast-global K-means clustering algorithm is used for quantization to overcome the problem that the original algorithm may choose noise points as the cluster center. Finally, Huffman coding is used to reduce the code length of the quantized data. Simulation results show that the proposed algorithm has good CR and EVM performance within the error tolerance range. It has a high degree of compression and signal recovery.

The rapid development of 5G communication provides a huge opportunity for the research on the Internet of vehicles. With the increasing shortage of low-frequency spectrum resources and the demand for high transmission rates for the architecture of the internet of vehicles, the highly qualified millimeter wave has been combined into the research on the internet of vehicles. However, due to the various characteristics of millimeter wave and the limitation of the complex communication environment of the internet of vehicles, how to achieve stable beam tracking is a big problem that needs to be solved urgently. This paper mainly introduces an extended Kalman filter algorithm with updated threshold to solve the problem of continuous stable beam tracking of millimeter waveInternet of vehicles. Simulation shows that this method has lower tracking error than the original algorithm, and better overcomes the problem of continuous accumulation of tracking error of the original algorithm.

An intelligent lifestyle has become a hotspot for researchers and industries nowadays. The smart home monitoring and controlling system with the Arduino as the main controller is designed in this paper, combined with sensors, Wi-Fi, and cloud technologies. Various sensors collect household environmental information, such as indoor temperature and humidity, soil moisture, combustible gas concentration, and light intensity. The main controller processes the collected signals and automatically operates the devices, including a refrigeration equipment, water pump, buzzer, fan, stepping motor. The data can also be transmitted to the cloud platform through Wi-Fi for processing, and the home environment information and device can be remotely monitored and controlled by the cloud platform or smartphone APP.

With the development of smart home, speech recognition technology can provide a more convenient way for human–computer interaction, and meanwhile provides a better user experience. A voice control system for smart home based on single-chip computer module STC89C52 and speech recognition module LD3320 is designed in this paper. The electric equipment such as a fan can be controlled by the recognized voice command. The peripheral equipment equipped with a gas sensor, a smoke sensor, and a temperature and humidity sensor can gather the living environment in the room, and the temperature and humidity can be displayed on the OLED. A MP3 player equipped with a speaker can broadcast the voice once the fire or smoke is detected, then the user can be reminded by an alarm. The experiment results show that the system has high stability and high speech recognition rate, which can improve the level of disaster prevention.

With the rapid development of the mobile communication technology, the number of long-term evolution (LTE) users and the volume of data is continually increasing, which greatly increases the amount of data transferred by fronthaul, resulting in a huge investment in optical fiber resource. In order to control the consumption of optical fiber resource, lower the cost of operator and avoid congestion in the premise of increasing data volume, it is necessary to compress the data for fronthaul. According to the characteristics of the LTE baseband signal, this paper proposes a data compression scheme on the basis of discrete cosine transform (DCT) and vector quantization. Firstly, the time domain signal is transformed in DCT. And then, the k-means clustering algorithm is used for vector quantization on the transformed signal. Finally, the Huffman coding is applied to further increase the compression ratio (CR) under the promise of an acceptable error. The simulation results show that the scheme proposed has a good performance in both compression ratio and error vector Magnitude (EVM) for the LTE baseband signal base on orthogonal frequency division multiplexing (OFDM).

A smart lighting system uses the concept of green, low-carbon, energy-saving and environmental protection to change people’s lifestyle and bring about high-quality life. This paper designs a smart lighting control system based on Bluetooth and Android. When the system is in automatic mode, the pyroelectric infrared sensor can be used to detect whether anyone is passing by to control the LED light on or off. The LED light can be also controlled by the Android app through Bluetooth communication when the system is in manual mode. The testing results show that the system is energy-saving, efficient and easy-to-use, and it can be widely used in various lighting scenes.

With the continuous development of electronic technology, the previous traditional methods of blood pressure testing are far from meeting people’s requirements. Fortunately, the advent of electronic blood pressure meters has shown great convenience and potential. Against the backdrop, a new electric blood pressure testing system based on a CC3200 LaunchPad is proposed in this paper, which uses a pressure sensor to collect the information of static pressure and fluctuation of blood pressure, and then transmits the processed data to the software in PC. The software in PC analyzes the data by oscillometric method and Gaussian fitting method, and then the static pressure, systolic pressure and diastolic pressure are calculated and displayed. Test results show that the blood pressure testing efficiency is greatly improved by this system with its high accuracy and reliability, which can meet the requirement of the AAMI (Association for the Advancement of Medical Instrumentation) standard.

With the increase of mobile equipment and transmission data, Common Public Radio Interface (CPRI) between Building Base band Unit (BBU) and Remote Radio Unit (RRU) suffers amounts of increasing transmission data. It is essential to compress the data in CPRI if more data should be transferred without congestion under the premise of restriction of fiber consumption. A data compression scheme based on Discrete Sine Transform (DST) and Lloyd-Max quantization is proposed in distributed Base Station (BS) architecture. The time-domain samples are transformed by DST according to the characteristics of Orthogonal Frequency Division Multiplexing (OFDM) baseband signals, and then the coefficients after transformation are quantified by the Lloyd-Max quantizer. The simulation results show that the proposed scheme can work at various Compression Ratios (CRs) while the values of Error Vector Magnitude (EVM) are better than the limits in 3GPP.

By separately transmitting data and control signals from different tiers, dual connectivity (DC) delivers a potential solution to the handover and load balancing challenges brought by small cell deployment. In this paper, a tractable framework is developed for the downlink performance analysis of DC assisted heterogeneous cellular networks (HetNets). Based on a stochastic geometry model, accurate expressions of data-control joint coverage probability and average rate for a typical DC capable UE are derived and investigated under DC mode and single connectivity (SC) mode. It is observed that operating parameters like cell association bias and transmit power will change the optimum node density ratio to reach the maximum coverage probability and average rate, but they have little impact on the levels of maximum coverage and rate performance. Numerical results also reveal that better coverage and rate performance can be expected in scenarios with higher small cell path loss exponent.

As the long term evolution (LTE) mobile users and transmission data increase, the load of the fronthaul network increases. In order to control the consumption of optical fiber resources, and prevent congestion under the premise of increasing data transmission, it is necessary to compress the data of the fronthaul network. In this paper, a data compression scheme of LTE-based fronthaul network is proposed. According to the characteristics of LTE baseband signals, discrete sine transform (DST) is applied to the time domain signals, and the transformed coefficients are partitioned according to the energy concentration characteristics. Bit allocation is performed in different blocks, and the coefficients of each block are quantized by Lloyd-Max quantizer. Finally, Huffman coding is carried out to improve the compression ratio under the condition that the error is allowed. The simulation results show that the proposed data compression scheme has good performance in both compression ratio (CR) and error vector magnitude (EVM).

Virtual multiple input multiple output (MIMO) technology has been widely researched in the aspect of energy conservation for wireless sensor network (WSN) communication. In this this paper, data transmission for the entire network based on virtual MIMO WSN structure is studied, combined with clustering and multi-hop transmission mode. Each cluster head (CH) is formed according to the clustering algorithm, and the data is transmitted to the adjacent CH by cooperative communication mode. The circuit energy consumption and the transmission energy consumption are both considered in our model, so the minimum inter-cluster transmission energy consumption is obtained, and the optimal path to the sink node is obtained. The proposed strategy does not need the receiver’s collaboration to avoid the complexity of the receiver; meanwhile more energy consumption of the network is saved from the perspective of the network life cycle. The simulation results show that the proposed strategy can reduce more energy consumption and prolong the network life cycle.