33 research outputs found
A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality
The Internet of Medical Things (IoMT) is a platform that combines Internet of
Things (IoT) technology with medical applications, enabling the realization of
precision medicine, intelligent healthcare, and telemedicine in the era of
digitalization and intelligence. However, the IoMT faces various challenges,
including sustainable power supply, human adaptability of sensors and the
intelligence of sensors. In this study, we designed a robust and intelligent
IoMT system through the synergistic integration of flexible wearable
triboelectric sensors and deep learning-assisted data analytics. We embedded
four triboelectric sensors into a wristband to detect and analyze limb
movements in patients suffering from Parkinson's Disease (PD). By further
integrating deep learning-assisted data analytics, we actualized an intelligent
healthcare monitoring system for the surveillance and interaction of PD
patients, which includes location/trajectory tracking, heart monitoring and
identity recognition. This innovative approach enabled us to accurately capture
and scrutinize the subtle movements and fine motor of PD patients, thus
providing insightful feedback and comprehensive assessment of the patients
conditions. This monitoring system is cost-effective, easily fabricated, highly
sensitive, and intelligent, consequently underscores the immense potential of
human body sensing technology in a Health 4.0 society
Prior knowledge-based deep learning method for indoor object recognition and application
Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision
PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST
We describe PSR J1926-0652, a pulsar recently discovered with the
Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive
single-pulse detections from FAST and long-term timing observations from the
Parkes 64-m radio telescope, we probed phenomena on both long and short time
scales. The FAST observations covered a wide frequency range from 270 to 800
MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at
least four profile components, short-term nulling lasting from 4 to 450 pulses,
complex subpulse drifting behaviours and intermittency on scales of tens of
minutes. While the average band spacing P3 is relatively constant across
different bursts and components, significant variations in the separation of
adjacent bands are seen, especially near the beginning and end of a burst. Band
shapes and slopes are quite variable, especially for the trailing components
and for the shorter bursts. We show that for each burst the last detectable
pulse prior to emission ceasing has different properties compared to other
pulses. These complexities pose challenges for the classic carousel-type
models.Comment: 13pages with 12 figure
A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing
Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time
A Service Recommendation Method Based on Requirements for the Cloud Environment
In the cloud computing environment, there are huge amounts of functionally similar cloud services. Additionally, user requirements can change. Therefore, it is difficult to recommend services that meet users’ requirements. To overcome the problems, a service recommendation method based on requirements is proposed. First, we form user communities by clustering to reduce the recommended range. Second, we use the reported QoS (Quality of Service) values and the evaluated QoS values to predict the QoS requirements of users. Third, based on the requirements, the matching degree of users to services is obtained. Finally, based on the similarity between the target user and the user’s neighbors and the difference in their matching degree of service and the ratings of services by the neighbors, we can obtain a list of service recommendations for the target user. Compared to the traditional collaborative filtering method and the deviation-based method, our method improves the recommendation accuracy without lowering the efficiency
A New Mining and Protection Method Based on Sensitive Data
The traditional method of sensitive data identification for data stream has a large amount of calculation and does not reflect the impact of time on the data value, and the mining accuracy is not high. In view of the above problems we firstly adopt the sliding window mechanism to divide the data flow according to time and delay the dataset according to the characteristics of the data flow in the sliding window to achieve the purpose of saving time and space. At the same time, threshold sensitivity analysis is used to find out the optimal threshold. Finally, a K-anonymous algorithm based on dynamic rounding function is employed to achieve the protection of sensitive data. Theoretical analysis and experimental results show that the algorithm can effectively mine the sensitive data in the data stream and can effectively protect the sensitive data
A novel differential privacy recommendation method based on distributed framework
With the rapid development of mobile Internet technology, the traditional recommender systems have not been well adapted to location-based recommendation services, and they also face the risk of privacy leaks. In this paper, a distributed privacy-preserving recommendation framework is proposed, and a singular value decomposition recommendation algorithm based on distributed framework is designed by using the differential privacy technique. Furthermore, we use an order-preserving encryption function to protect the locations of users' requests. Theoretical analysis and experimental evaluation on two real datasets show that the proposed method not only provides a stronger privacy protection, but also delivers a better recommendation performance than traditional recommendation algorithms