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1. 发明申请

US20230076452A1 METHOD AND SYSTEM FOR RECOGNIZING 2D MOVEMENT TRACK BASED ON SMART WATCH 有权
公开(公告)号：US20230076452A1
公开(公告)日：2023-03-09
申请号：US17792698
申请日：2020-09-07
申请人： SHENZHEN UNIVERSITY
发明人： Kaishun WU , Lin CHEN , Cong LI , Yandao HUANG
IPC分类号： H04L12/28 , H04M1/72448 , H04L67/12
摘要： 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.

2. 发明申请

US20210240806A1 VIBRATION SIGNAL-BASED SMARTWATCH AUTHENTICATION METHOD 有权
公开(公告)号：US20210240806A1
公开(公告)日：2021-08-05
申请号：US17035752
申请日：2020-09-29
申请人： SHENZHEN UNIVERSITY
发明人： Kaishun WU , Yandao HUANG , Lin CHEN
IPC分类号： G06F21/31 , G06F1/16 , G06K9/62 , G01M7/02 , G06F9/30
摘要： 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.

3. 发明公开

US20240121136A1 DEEP LEARNING METHOD AND SYSTEM FOR SPECTRUM SHARING OF PARTIALLY OVERLAPPING CHANNELS 审中-公开
公开(公告)号：US20240121136A1
公开(公告)日：2024-04-11
申请号：US18281418
申请日：2021-03-26
申请人： Shenzhen University
发明人： Lu WANG , Ruifeng HUANG , Kaishun WU
IPC分类号： H04L25/02 , H04W28/082
CPC分类号： H04L25/0254 , H04W28/082 , H04L5/0057
摘要： 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.

4. 发明申请

US20230119957A1 MASSIVE MIMO WIRELESS ENERGY TRANSMISSION METHOD BASED ON DYNAMIC FRAME TRANSMISSION 有权
公开(公告)号：US20230119957A1
公开(公告)日：2023-04-20
申请号：US17955325
申请日：2022-09-28
申请人： Shenzhen University
发明人： Yueling CHE , Yishen LI , Liangzhu LI , Sheng LUO , Kaishun WU
IPC分类号： H04B7/0456 , H04L27/26
摘要： 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.

5. 发明公开

US20240031427A1 CLOUD-NETWORK INTEGRATION ORIENTED MULTI-ACCESS EDGE COMPUTING ARCHITECTURE 审中-公开
公开(公告)号：US20240031427A1
公开(公告)日：2024-01-25
申请号：US18476269
申请日：2023-09-27
申请人： SHENZHEN UNIVERSITY
发明人： Lu WANG , Jianhao ZHANG , Kaishun WU , Ting WANG
IPC分类号： H04L67/1001 , H04W72/542
CPC分类号： H04L67/10015 , H04W72/542
摘要： 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.

6. 发明申请

US20220188392A1 METHOD FOR USER RECOGNITION AND EMOTION MONITORING BASED ON SMART HEADSET 有权
公开(公告)号：US20220188392A1
公开(公告)日：2022-06-16
申请号：US17283966
申请日：2021-03-04
申请人： SHENZHEN UNIVERSITY
发明人： Yongpan ZOU , Haibo LEI , Kaishun WU
IPC分类号： G06F21/32 , H04R1/08 , H04R1/10 , A61B7/04 , A61B5/117 , A61B5/16 , A61B5/00
摘要： 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.

7. 发明申请

US20200226369A1 INTERACTIVE METHOD AND INTERACTIVE SYSTEM FOR SMART WATCH 审中-公开
公开(公告)号：US20200226369A1
公开(公告)日：2020-07-16
申请号：US16731042
申请日：2019-12-31
申请人： SHENZHEN UNIVERSITY
发明人： Kaishun WU
IPC分类号： G06K9/00 , G06K9/62
摘要： An interactive method of a smart watch includes the following steps: S1. acquiring a vibration signal of an accelerometer and a gyroscope of a smart watch based on a vibration signal transmitted by a human body; S2. identifying the vibration signal by using an anomaly detection algorithm; S3. pre-processing the vibration signal, and further classifying and identifying the vibration signal by using an improved algorithm of a k-nearest neighbor algorithm; S4. 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.

8. 发明申请

US20160274230A1 WIRELESS NETWORK BASED METHOD FOR DETECTING PRESENCE OF METAL 审中-公开
标题翻译：用于检测金属存在的基于无线网络的方法
公开(公告)号：US20160274230A1
公开(公告)日：2016-09-22
申请号：US14932867
申请日：2015-11-04
申请人： SHENZHEN UNIVERSITY
发明人： Kaishun WU , Yuanchao AN , Zhong MING
IPC分类号： G01S13/88 , G08B21/00 , G01S13/04
CPC分类号： G01S13/887 , G01S7/412 , G01S13/003
摘要： A wireless network based method for detecting carrying of metal, comprising: installing a wireless signal transmitting device and a wireless signal receiving device indoors; continuously transmitting to the surrounding, by the wireless signal transmitting device, wireless electromagnetic wave signals; continuously receiving, by the wireless signal receiving device, the wireless electromagnetic wave signals; continuously performing, through a signal abnormity detection system, abnormity analysis to the received wireless electromagnetic wave signals, and if any abnormality is determined, performing further analysis in the next step S5 to wireless electromagnetic wave signals within an abnormal waveband; and S5: extracting, through a date feature analysis system, a feature value of the wireless electromagnetic wave signals within the abnormal waveband, comparing and matching the feature value with features stored in an abnormal feature library, and if the feature of the wireless electromagnetic wave signals matches the feature of metal successfully, triggering an alarm device.
摘要翻译：一种用于检测金属携带的无线网络方法，包括：在室内安装无线信号发射装置和无线信号接收装置; 通过无线信号发射装置连续向周围传送无线电磁波信号; 通过无线信号接收装置连续地接收无线电磁波信号; 通过信号异常检测系统对接收到的无线电波信号进行异常分析，如果确定有异常，则在下一步骤S5中进一步分析异常波段内的无线电波信号; S5：通过日期特征分析系统提取异常波段内的无线电磁波信号的特征值，将特征值与存储在异常特征库中的特征进行比较和匹配，并且如果无线电磁波的特征信号成功匹配金属功能，触发报警装置。

9. 发明公开

US20230326249A1 FEW-SHOT GESTURE RECOGNITION METHOD 审中-公开
公开(公告)号：US20230326249A1
公开(公告)日：2023-10-12
申请号：US17940266
申请日：2022-09-08
申请人： Shenzhen University
发明人： Yongpan ZOU , Haozhi DONG , Yaqing WANG , Kaishun WU
IPC分类号： G06V40/20 , G06V10/74 , G06V10/764
CPC分类号： G06V40/20 , G06V10/74 , G06V10/764
摘要： 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.

10. 发明申请

US20220391023A1 HUMAN-COMPUTER INTERACTION METHOD AND INTERACTION SYSTEM BASED ON CAPACITIVE BUTTONS 有权
公开(公告)号：US20220391023A1
公开(公告)日：2022-12-08
申请号：US17888990
申请日：2022-08-16
申请人： Shenzhen University
发明人： Kaishun WU , Maoning GUAN
IPC分类号： G06F3/02 , H03K17/96
摘要： 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.

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