Patent portfolio analysis model based on legal status information

Purpose:This research proposes a patent portfolio analysis model based on the legal status information to chart out a competitive landscape in a particular field,enabling organizations to position themselves within the overall technology landscape.Design/methodology/approach:Three indicators were selected for the proposed model:Patent grant rate,valid patents rate and patent maintenance period.The model uses legal status information to perform a qualitative evaluation of relative values of the individual patents,countries or regions’ technological capabilities and competitiveness of patent applicants.The results are visualized by a four-quadrant bubble chart To test the effectiveness of the model,it is used to present a competitive landscape in the lithium ion battery field.Findings:The model can be used to evaluate the values of the individual patents,highlight countries or regions’ positions in the field,and rank the competitiveness of patent applicants in the field.Research limitations:The model currently takes into consideration only three legal status indicators.It is actually feasible to introduce more indicators such as the reason for invalid patents and the distribution of patent maintenance time and associate them with those in the proposed model.Practical implications:Analysis of legal status information in combination of patent application information can help an organization to spot gaps in its patent claim coverage,as well as evaluate patent quality and maintenance situation of its granted patents.The study results can be used to support technology assessment,technology innovation and intellectual property management.Originality/value:Prior studies attempted to assess patent quality or competitiveness by using either single patent legal status indicator or comparative analysis of the impacts of each indicator.However,they are insufficient in presenting the combined effects of the evaluation indicators.Using our model,it appears possible to get a more complete and objective picture of the current competitive situation.

Crowd Counting Based on WiFi Channel State Information and Transfer Learning

With the popularity and development of indoor WiFi equipment, they have more sensing capability and can be used as a human monitoring device. We can collect the channel state information (CSI) from WiFi device and acquire the human state based on the measurements. These studies have attracted wide attention and become a hot research topic. This paper concentrated on the crowd counting based on CSI and transfer learning. We utilized the CSI signal fluctuations caused by human motion in WiFi coverage to identify the person count because different person counts would lead to unique signal propagation characteristics. First, this paper presented recent studies of crowd counting based on CSI. Then, we introduced the basic concept of CSI, and described the fundamental principle of CSI-based crowd counting. We also presented the system framework, experiment scenario, and neural network structure transferred from the ResNet. Next, we presented the experiment results and compared the accuracy using different neural network models. The system achieved recognition accuracy of this 100 percent for seven participants using the transfer learning technique. Finally, we concluded the paper by discussing the current problems and future work.

Behavior Recognition of the Elderly in Indoor Environment Based on Feature Fusion of Wi-Fi Perception and Videos

With the intensifying aging of the population,the phenomenon of the elderly living alone is also increasing.Therefore,using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study.Video-based action recognition tasks are easily affected by object occlusion and weak ambient light,resulting in poor recognition performance.Therefore,this paper proposes an indoor human behavior recognition method based on wireless fidelity(Wi-Fi)perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process.This paper uses the public WiFi-based activity recognition dataset(WIAR)containing Wi-Fi channel state information and essential action videos,and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms,respectively.Then the two sets of feature vectors are fused,and finally,the action classification and recognition are performed by the support vector machine(SVM).The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments.And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10%on average.

Indoor Human Fall Detection Algorithm Based on Wireless Sensing

As the main health threat to the elderly living alone and performing indoor activities,falls have attracted great attention from institutions and society.Currently,fall detection systems are mainly based on wear sensors,environmental sensors,and computer vision,which need to be worn or require complex equipment construction.However,they have limitations and will interfere with the daily life of the elderly.On the basis of the indoor propagation theory of wireless signals,this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior.The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive,robustness,universality,and low price.The module combines digital signal processing technology and machine learning technology.This paper analyzes and processes the channel state information(CSI)data of wireless signals,and the local outlier factor algorithm is used to find the abnormal CSI sequence.The support vector machine and extreme gradient boosting algorithms are used for classification,recognition,and comparative research.Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%.This work has important social significance in ensuring the safety of the elderly.

A flexible scheduling algorithm for the 5th-generation networks

At present,the 5th-Generation(5G)wireless mobile communication standard has been released.5G networks efficiently support enhanced mobile broadband traffic,ultra-reliable low-latency communication traffic,and massive machine-type communication.However,a major challenge for 5G networks is to achieve effective Radio Resource Management(RRM)strategies and scheduling algorithms to meet quality of service requirements.The Proportional Fair(PF)algorithm is widely used in the existing 5G scheduling technology.In the PF algorithm,RRM assigns a priority to each user which is served by gNodeB.The existing metrics of priority mainly focus on the flow rate.The purpose of this study is to explore how to improve the throughput of 5G networks and propose new scheduling schemes.In this study,the package delay of the data flow is included in the metrics of priority.The Vienna 5G System-Level(SL)simulator is a MATLAB-based SL simulation platform which is used to facilitate the research and development of 5G and beyond mobile communications.This paper presents a new scheduling algorithm based on the analysis of different scheduling schemes for radio resources using the Vienna 5G SL simulator.