A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

An Adaptive Tag Anti-Collision Protocol in RFID Wireless Systems

A novel anti-collision algorithm in RFID wireless network is proposed.As it is put forward on the basis of collision tree(CT)and improved collision tree(lCT) anti-collision protocols,we call it adaptive collision tree protocol(ACT).The main novelty of this paper is that the AD strategy is introduced and used in ACT to decrease collisions and improve the tag system throughput.AD strategy means that query strings will divide into two or four branches adaptively according to the label quantity.This scheme can decrease both depth of query and collision timeslots,and avoid producing too much idle timeslots at the same time.Both theoretical analysis and simulation results indicate that the novel proposed anticollision protocol ACT outperforms the previous CT and ICT protocols in term of time complexity,system throughput,and communication complexity.

Design of Anti-Collision Integrated Security Mechanism Based on Chaotic Sequence in UHF RFID System

Collision and security issues are considered as barriers to RFID applications.In this paper,a parallelizable anti-collision based on chaotic sequence combined dynamic frame slotted aloha to build a high-efficiency RFID system is proposed.In the tags parallelizable identification,we design a Discrete Markov process to analyze the success identification rate.Then a mutual authentication security protocol merging chaotic anti-collision is presented.The theoretical analysis and simulation results show that the proposed identification scheme has less than 45.1%of the identification time slots compared with the OVSF-system when the length of the chaos sequence is 31.The success identification rate of the proposed chaotic anti-collision can achieve 63%when the number of the tag is100.We test the energy consumption of the presented authentication protocol,which can simultaneously solve the anti-collision and security of the UHF RFID system.

A Physical Layer Algorithm for Estimation of Number of Tags in UHF RFID Anti-Collision Design

A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access protocols.Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful slots.Usually,a collision detection algorithm is employed to determine types of time slots.Only three types are distinguished because of lack of ability to detect the number of tags in single time slot.In this paper,a physical layer algorithm is proposed to detect the number of tags in a collided slot.Mean shift algorithm is utilized,and some properties of backscatter signals are investigated.Simulation results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches.

A Novel Time-aware Frame Adjustment Strategy for RFID Anti-collision

Recently,object identification with radio frequency identification(RFID)technology is becoming increasingly popular.Identification time is a key performance metric to evaluate the RFID system.The present paper analyzes the deficiencies of the state-of-the-arts algorithms and proposes a novel sub-frame-based algorithm with adaptive frame breaking policy to lower the tag identification time for EPC global C1 Gen2 UHF RFID standard.Through the observation of slot statistics in a sub-frame,the reader estimates the tag quantity and efficiently calculates an optimal frame size to fit the unread tags.Only when the expected average identification time in the calculated frame size is less than that in the previous frame size,the reader starts the new frame.Moreover,the estimation of the proposed algorithm is implemented by the look-up tables,which allows dramatically reduction in the computational complexity.Simulation results show noticeable throughput and time efficiency improvements of the proposed solution over the existing approaches.

An Improved Binary Search Anti-Collision Protocol for RFID Tag Identification

Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.

Research on the adaptive hybrid search tree anti-collision algorithm in RFID system

Due to more tag-collisions result in failed transmissions,tag anti-collision is a very vital issue in the radio frequency identification(RFID) system.However,so far decreases in communication time and increases in throughput are very limited.In order to solve these problems,this paper presents a novel tag anti-collision scheme,namely adaptive hybrid search tree(AHST),by combining two algorithms of the adaptive binary-tree disassembly(ABD) and the combination query tree(CQT),in which ABD has superior tag identification velocity and CQT has optimum performance in system throughput and search timeslots.From the theoretical analysis and numerical simulations,the proposed algorithm can colligate the advantages of above algorithms,improve the system throughput and reduce the searching timeslots dramatically.

Performance Analysis of RFID Framed Slotted Aloha Anti-Collision Protocol

In this paper, we develop a novel mathematical model to estimate the probability distribution function of the number of tags discovered after a certain number of interrogation rounds. In addition, the pdfs of the number of rounds needed to discover all the tags are also calculated. The estimation of such pdfs will be helpful in estimating the number of interrogation rounds and the optimal parameter configuration of the RFID system which in turn will be helpful in estimating the time needed to discover all tags. Our results show that the proposed model accurately predicts the tags detection probability. We then use the proposed model to optimally configure the reader parameters (i.e. the frame size and the number of interrogation rounds).

Distance-Measuring Technology Using LD for Auto Anti-Collision

Auto anti-collision technology is one of the main research subjects of automobiles’ safety technology. Aiming at the key technology of Auto anti-collision, measuring the distance from obstacles, based on the theory of phase laser distance ranging, Laser Diode (LD) distance-measuring system for auto anti-collision has been developed to solve the problem of on-line measuring distance technology in middle to long distance utilizing the good characteristics of LD when modulating its optical intensity and adopting typical kinds of filter techniques in this paper. By theoretical analysis, adopting typical kinds of filter techniques can reduce the interference of strong light, so distance-measuring range can be 0.5–100 m in daytime or 1–200 m at night. And more, from theoretical analysis and experiment result, it can guarantee the high measuring resolution which can be less than 24.5 mm, utilizing the method of two Laser Diode optical intensity modulating wavelength and complimenting precise calibration and revision. The idea of LD distance-measuring technology is novel and feasible and this technology can be applied in Auto anti-collision. Key words laser diode - phase laser distance ranging - filter techniques - auto anti-collision CLC number TH 161 Foundation item: Supported by the National Natural Science Foundation of China (59675080, 59805006) and Wuhan Chenguang Foundation (20025001001)Biography: Zhang Xin-bao (1965-), male, Associate professor, research direction: precise mechanism and instrument.

A Frame Breaking Based Hybrid Algorithm for UHF RFIDAnti-Collision

Multi-tag collision imposes a vital detrimental effect on reading performanceof an RFID system. In order to ameliorate such collision problem and to improve thereading performance, this paper proposes an efficient tag identification algorithm termedas the Enhanced Adaptive Tree Slotted Aloha (EATSA). The key novelty of EATSA is toidentify the tags using grouping strategy. Specifically, the whole tag set is divided intogroups by a frame of size F. In cases multiple tags fall into a group, the tags of the groupare recognized by the improved binary splitting (IBS) method whereas the rest tags arewaiting in the pipeline. In addition, an early observation mechanism is introduced toupdate the frame size to an optimum value fitting the number of tags. Theoretical analysisand simulation results show that the system throughput of our proposed algorithm canreach as much as 0.46, outperforming the prior Aloha-based protocols.

A Dynamic Multi-ary Query Tree Protocol for Passive RFID Anti-collision

In this paper,a dynamic multi-ary query tree(DMQT)anti-collision protocol for Radio Frequency Identification(RFID)systems is proposed for large scale passive RFID tag identification.The proposed DMQT protocol is based on an iterative process between the reader and tags which identifies the position of collision bits through map commands and dynamically encodes them to optimize slots allocation through query commands.In this way,the DMQT completely eliminates empty slots and greatly reduces collision slots,which in turn reduces the identification time and energy costs.In addition and differently to other known protocols,the DMQT does not need to estimate the number of tags,reducing the protocol implementation complexity and eliminating the uncertainty caused by the estimation algorithm.A numerical analysis shows that DMQT has better performance than other algorithms for a number of tags larger than 300.Meanwhile,when the number of tags is 2000 and the tag identity(ID)length is 128 bits,the total identification time is 2.58 s and the average energy cost for a tag identification is 1.2 mJ,which are 16.9%and 10.4%less than those of state-of-the-art algorithms,respectively.In addition,a DMQT extension based on ACK command has also been presented to deal with capture effect and avoid missing identification.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Synchronous Dynamic Adjusting: An Anti-Collision Algorithm for an RF-UCard System

An RF-UCard system is a contactless smartcard system with multiple chip operating systems and multiple applications. A multi-card collision occurs when more than one card within the reader’s read field and thus lowers the efficiency of the system. This paper presents a novel and enhanced algorithm to solve the multi-card collision problems in an RF-UCard system. The algorithm was originally inspired from framed ALOHA-based anti-collision algorithms applied in RFID systems. To maximize the system efficiency, a synchronous dynamic adjusting (SDA) scheme that adjusts both the frame size in the reader and the response probability in cards is developed and evaluated. Based on some mathematical results derived from the Poisson process and the occupancy problem, the algorithm takes the estimated card quantity and the new arriving cards in the current read cycle into consideration to adjust the frame size for the next read cycle. Also it changes the card response probability according to the request commands sent from the reader. Simulation results show that SDA outperforms other ALOHA-based anti-collision algorithms applied in RFID systems.

Application of Ship Maneuvering Simulator's Technology on Research of Vessels Automatic Anti-Collision

In this paper, the authors make a summary of current situation of research on the Auto Anti-Collision, briefly introduce the components and functions of the NSACA Simulation Platform and bring forward the project to realize the simulation of an Automatic Anti-Collision control, based on the NSACA Simulation Platform. Finally give typical examples from a great deal of simulating tests and analyze them.

Novel Tag Anti-Collision Algorithm with Adaptive Grouping

For RFID tags, a Novel Tag Anti-collision Algorithm with Grouping (TAAG) is proposed. It divides tags into groups and adopts a deterministic method to identify tags within group. TAAG estimates the total number of tags in systems from group identifying result and then adjusts the grouping method accordingly. The performance of the proposed TAAG algorithm is compared with the conventional tag anti-collision algorithms by simulation experiments. According to both the analysis and simulation result, the proposed algorithm shows better performance in terms of throughput, total slots used to identify and total cycles.

Research on Anti-collision Algorithm of Short Distance Data communication based on Binary-Tree Disassembly

This paper puts forward adaptive anti collision algorithm based on two fork tree decomposition. New search algorithm built on the basis of binary-tree algorithm, using the uniqueness of the label EPC, to estimate the distribution of label by slot allocation, the huge and complicated two fork tree is decomposed into several simple binary-tree by search the collision slots for binary-tree, so, it can simplifies the search process. The algorithm fully considers4 important performance parameters of the reader paging times, transmission delay, energy consumption and throughput label, the simulation results show that, the improved anti-collision algorithm is obviously improved performance than other two fork tree algorithm, it is more suitable for RFID anti-collision protocols.

Research on RFID Anti-Collision in Multi-label Environment

This paper introduces several common binary tree anti-collision algorithms. They are binary tree algorithm, dynamic binary tree algorithm, and backward binary tree algorithm. Then, based on these algorithms, an improved binary tree algorithm is proposed. Simulation and results analysis show that the improved binary anti-collision algorithm has higher performance than other binary tree algorithms, and improves the efficiency of RFID card reader searching card in multi-label environment.

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。