Shenzhen University

Shenzhen Unnersity

Kaishun Wu

Lu Wang

Yongpan Zou

Lin Chen

Yandao Huang

Sheng Luo

Jinyong Wu

Yueling Che

Maoning Guan

Jun Xu

Cong Li

Ting Wang

Zhong Ming

Dan Wang

Kaixin Chen

Yuanchao An

Yongzhi Huang

Wenkai Yang

Jie Ouyang

Yabin Lai

Qianru Liao

Yishen Li

Liangzhu Li

Haibo Lei

Jianhao Zhang

Ruifeng Huang

Junjun Bao

Yaqing Wang

Junmei Yao

Hailiang Yang

Haozhi Dong

The present application discloses a deep learning method and system for spectrum sharing of partially overlapping channels. The method comprises the following steps: in response to a received user transmission request, inputting, by a base station, channel state information CSI of a plurality of historical time slices into a trained channel prediction convolutional neural network model and outputting predicted channel state information CSI of a next time slice; and inputting the channel state information CSI of the next time slice into a reinforcement learning model and obtaining a channel allocation strategy of each user equipment in a collision domain of the base station so as to realize a maximum throughput of simultaneous communication of each user equipment, wherein the reinforcement learning model is obtained by training by taking bandwidth efficiency performance as a reward. The present application is high in universality, bandwidth utilization rate and throughput for communication networks.

Deep learning method and system for spectrum sharing of partially overlapping channels

A method and a system for recognizing a two-dimensional (2D) movement track based on a smart watch is provided. The method comprises: acquiring accelerometer signal data and gyroscope signal data of the smart watch; estimating a tilt angle of the smart watch by using the accelerometer signal data and correcting the gyroscope signal data by using the tilt angle; and calculating angle value information of the smart watch by using the corrected gyroscope signal data and estimating a coordinate point. According to the present application, the movement track of the smart watch can be accurately estimated by using the accelerometer and the gyroscope built in the smart watch.

Method and system for recognizing 2D movement track based on smart watch

Disclosed is a few-shot gesture recognition method. The method comprises the following steps: customizing, by a user, gesture categories, and acquiring few samples for each gesture category; inputting the acquired samples into a trained few-shot learning model, extracting a feature vector corresponding to each sample, and synthesizing feature vectors belonging to the same gesture to obtain an average feature vector corresponding to each gesture as a prototype vector; acquiring a corresponding sample for a target gesture implemented by the user, and inputting the sample into the few-shot learning model to obtain a feature vector of the target gesture as a query vector; and calculating similarities between the query vector and prototype vectors of different gestures, and selecting a gesture category corresponding to the prototype vector with the highest similarity as a prediction category of the target gesture.

Few-shot gesture recognition method

The present application discloses a cloud-network integration (CNI) oriented multi-access edge computing (MEC) architecture. An access network (AN) side of the architecture is provided with a plurality of edge computing nodes, a physical channel of the AN side is split into a plurality of subchannels with each of the subchannels supporting a media access control (MAC) access mode, and a software defined network (SDN) controller is arranged in the architecture and is configured to allocate resources of the subchannels at a physical layer and protocols at an MAC layer and control offloading at the edge computing nodes or a cloud center by a terminal user. The present application can carry out fine-grained control and cooperative management on network resources and computing resources, and achieve more effective computation offloading and service enhancement.

Cloud-network integration oriented multi-access edge computing architecture

A method for user recognition and emotion monitoring based on a smart headset is provided. The smart headset includes an earplug part and a main body, wherein the earplug part is provided with a first microphone and a wearing detection sensor, and a housing of the main body is provided with a signal amplification circuit, a communication module, and a microcontroller. The wearing detection sensor is to detect whether a user wears the smart headset, and the first microphone is to obtain a sound signal in an ear canal. The sound signal is amplified, and then is outputted to the microcontroller. The amplified sound signal is transmitted by the microcontroller to a smart terminal paired with the smart headset to extract a heart sound signal characteristic, and legality of—identity of the user is validated and emotional state of the user is inferred according to the heart sound signal characteristic.

Method for user recognition and emotion monitoring based on smart headset

The present application discloses a massive MIMO wireless energy transmission method based on dynamic frame transmission. The method comprises the following steps: controlling, by a base station, each antenna to transmit a pilot signal to a user end in a time-sharing mode by using set time-sharing pilot frames; acquiring, by the user end, downlink channel state information from the antennae of the base station to the user end and feeding the downlink channel state information back to the base station; and calculating, by the base station, a precoding matrix based on the downlink channel state information, mapping data from a user layer to an antenna port by using the newly calculated precoding matrix, and performing beam forming calculation with maximization of an energy signal of the user end as a goal.

Massive MIMO wireless energy transmission method based on dynamic frame transmission

The present application discloses a human-computer interaction method and an interaction system based on capacitive buttons. The method comprises the following steps: acquiring a capacitive charging signal generated by a user finger-touching a capacitive button; processing the capacitive charging signal to extract Mel-frequency cepstrum coefficient characteristics; and inputting the Mel-frequency cepstrum coefficient characteristics into a trained hidden Markov model, identifying a finger type of the user touching the capacitive button, and further implementing human-computer interaction according to an identification result. The present application utilizes the capacitive charging signals generated by touching the capacitive buttons with different fingers to perform button interaction, thus solving the problem of difficult interaction of capacitive buttons, and the present application trains a hidden Markov chain model by taking the Mel-frequency cepstrum coefficient of the capacitive charging signals as the characteristic, thereby improving the identification accuracy.

Human-computer interaction method and interaction system based on capacitive buttons

A vibration signal-based smartwatch authentication method includes generating incremental vibration signals using a vibration motor in a smartwatch; performing frequency band-based hierarchical endpoint segmentation to obtain vibration signals at a plurality of frequency bands; extracting frequency-domain features for the vibration signals at the plurality of frequency bands; training a dynamic time warping model by taking the vibration signals at the plurality of frequency bands as a training data set, training a nearest neighbor model by taking the extracted frequency-domain features as training data; collecting to-be-authenticated vibration signals which are processed to serve as test data signals; discriminating similarities between the test data signals and corresponding training data signals through the dynamic time warping model, giving a classification result through the nearest neighbor model, performing weighted calculation on a discrimination result of the dynamic time warping model and a discrimination result of the nearest neighbor model to obtain an authentication result.

Vibration signal-based smartwatch authentication method

A monitoring method and system based on a magnetic field of a loudspeaker are provided. This method includes: collecting a magnetic field signal near the loudspeaker, and converting the magnetic field signal into a magnetic field digital signal; converting the magnetic field digital signal into a speech signal and removing an interference from the speech signal to obtain a filtered speech signal; removing noise in the speech signal, and performing short-time inverse Fourier transform on a speech power spectrum to obtain a denoised speech signal; equalizing response of the denoised speech signal to each frequency to convert the speech signal into an intelligible speech audio signal; and recognizing the speech audio signal and converting the same into a text content.

Monitoring method and system based on magnetic field of loudspeaker

A smart device input method based on facial vibration includes: collecting a facial vibration signal generated when a user performs voice input; extracting a Mel-frequency cepstral coefficient from the facial vibration signal; and taking the Mel-frequency cepstral coefficient as an observation sequence to obtain text input corresponding to the facial vibration signal by using a trained hidden Markov model. The facial vibration signal is collected by a vibration sensor arranged on glasses. The vibration signal is processed by: amplifying the collected facial vibration signal; transmitting the amplified facial vibration signal to the smart device via a wireless module; and intercepting a section from the received facial vibration signal as an effective portion and extracting the Mel-frequency cepstral coefficient from the effective portion by the smart device.

Smart device input method based on facial vibration

An optimization method for UAV-based wireless information and energy transmission includes following steps: S1: reporting, by a wireless device, an energy state of the wireless device to a UAV; S2: detecting, by the UAV, a channel state between the UAV and the wireless device; and S3: selecting, by the UAV, an optimal action based on estimated revenue maximization according to an electric quantity of the UAV, an electric quantity of the wireless device, and the channel state. The use of the wireless device can reduce wiring costs, beautify the space, and ensure a smaller size and a lower power. By applying the UAV to information and energy transmission for the wireless devices, the data transmission rate and the energy conversion efficiency of networks are improved.

Optimization method for UAV-based wireless information and energy transmission

A vibration-based authentication method for an access control system includes: collecting vibration signals generated by a built-in vibration motor in an authentication device; filtering, denoising, and performing endpoint segmentation on the collected vibration signals, and extracting vibration signals containing effective touch; performing an alignment on the segmented vibration signals; performing a fast Fourier transform on the aligned vibration signals to obtain frequency-domain data, extracting frequency-domain features obtained after alignment and features obtained before alignment to construct a training data set, and storing the training data set in a database of the authentication device; using a new unlock signal generated when a user touches the authentication device as test data, and processing the test data to obtain test data containing effective touch; and matching and classifying the test data containing effective touch with the training data set by using a machine learning classification model, to obtain an authentication result.

Vibration-based authentication method for access control system

An auxiliary sensing method and system based on sensory substitution includes: establishing a discrete horizontal plane included angle and a normal vector included angle in three-dimensional space as well as response functions corresponding to different discrete distances, and establishing an acoustic coding library of an object; obtaining a surrounding environmental video; obtaining object information and category of current environment; establishing a three-dimensional sound field of the object according to the image information, the object information and the three-dimensional coordinate information obtained in combination with the acoustic coding library of the object; and adjusting, for objects of different categories, the three-dimensional sound field of each object according to movement information and variations of images, and playing the three-dimensional sound field of each object in real time.

Auxiliary sensing method and system based on sensory substitution

An interactive method of a smart watch includes the following steps: S. acquiring a vibration signal of an accelerometer and a gyroscope of a smart watch based on a vibration signal transmitted by a human body; S. identifying the vibration signal by using an anomaly detection algorithm; S. pre-processing the vibration signal, and further classifying and identifying the vibration signal by using an improved algorithm of a k-nearest neighbor algorithm; S. analyzing the user's feedback on the results and correcting them in time to maintain a stable identification accuracy. The present disclosure also discloses an interactive system, including a signal detection module, an identification and classification module, and a real-time feedback module. In the present disclosure, the human body part is regarded as a virtual screen based on the vibration signal transmitted by the human body.

Interactive method and interactive system for smart watch

A method and system for cross-technology communication from a WiFi device to a ZigBee device includes: generating, by a WiFi transmitter, to-be-transmitted symbol-level energy modulation bits information simultaneously carrying WiFi data bits and cross-technology data bits transmitted to the ZigBee device, wherein the cross-technology data bits are obtained based on symbol-level energy modulation; and a ZigBee receiver processing received signal strength indication sample information and initiating a cross-technology communication receiving process to obtain the cross-technology data bits needing to be received, and meanwhile a WiFi receiver obtaining the to-be-transmitted symbol-level energy modulation bits information through a standard WiFi receiving process, and then initiating a WiFi data recovery process to obtain original WiFi data bits.

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Shenzhen, China

Kaishun Wu