Efficient and accurate identification of missing tags for large-scale dynamic RFID systems

2022 ◽  
pp. 102394
Author(s):  
Xinning Chen ◽  
Kehua Yang ◽  
Xuan Liu ◽  
Ying Xu ◽  
Juan Luo ◽  
...  
Keyword(s):  
Large Scale ◽  
Accurate Identification ◽  
Rfid Systems

scholarly journals Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

2021 ◽  
Vol 13 (8) ◽  
pp. 1563
Author(s):  
Yuanyuan Tao ◽  
Qianxin Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Data Availability ◽  
Function Evaluation ◽  
Characteristic Analysis ◽  
Accurate Identification ◽  
Living Space ◽  
Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

scholarly journals Randomized Multi-Channel Interrogation Algorithm for Large-Scale RFID Systems

2009 ◽  
Author(s):  
Amir-Hamed Mohsenian-Rad ◽  
Vahid Shah-Mansouri ◽  
Vincent W. S. Wong ◽  
Robert Schober
Keyword(s):  
Large Scale ◽  
Rfid Systems

A Time-Efficient Protocol for Unknown Tag Identification in Large-Scale RFID Systems

2021 ◽  
pp. 1-1
Author(s):  
Chu Chu ◽  
Jianyu Niu ◽  
Wenxian Zheng ◽  
Jian Su ◽  
Guangjun Wen
Keyword(s):  
Large Scale ◽  
Tag Identification ◽  
Rfid Systems

scholarly journals iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

2021 ◽  
Vol 22 (16) ◽  
pp. 8958
Author(s):  
Phasit Charoenkwan ◽  
Chanin Nantasenamat ◽  
Md. Mehedi Hasan ◽  
Mohammad Ali Moni ◽  
Pietro Lio’ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
De Novo ◽  
Predictive Performance ◽  
Support Vector ◽  
Sufficient Information ◽  
Self Assessment ◽  
Accurate Identification ◽  
Bitter Peptides ◽  
Accurate Performance

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

Channel-Oblivious Counting Algorithms for Large-Scale RFID Systems

2015 ◽  
Vol 26 (12) ◽  
pp. 3303-3316 ◽  
Author(s):  
Wai Kit Sze ◽  
Yulin Deng ◽  
Wing Cheong Lau ◽  
Murali Kodialam ◽  
Thyaga Nandagopal ◽  
...  
Keyword(s):  
Large Scale ◽  
Rfid Systems ◽  
Counting Algorithms

Fast Tag Searching Protocol for Large-Scale RFID Systems

2013 ◽  
Vol 21 (3) ◽  
pp. 924-934 ◽  
Author(s):  
Yuanqing Zheng ◽  
Mo Li
Keyword(s):  
Large Scale ◽  
Rfid Systems

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

2020 ◽  
Author(s):  
Yu Wang ◽  
ZAHEER ULLAH KHAN ◽  
Shaukat Ali ◽  
Maqsood Hayat
Keyword(s):  
Feature Selection ◽  
Bacterial Infections ◽  
Large Scale ◽  
Computational Models ◽  
Genetic Material ◽  
Feature Space ◽  
Classification Model ◽  
Antibacterial Drug ◽  
Accurate Identification ◽  
Multi Level

Abstract BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

An Anticollision Protocol for Large-Scale Single-Reader RFID Systems

2013 ◽  
Vol 12 (3) ◽  
pp. 490-493
Author(s):  
Hao Jutao ◽  
Chen Qingkui ◽  
Yan Xiaolian
Keyword(s):  
Large Scale ◽  
Rfid Systems

Fast and Reliable Dynamic Tag Estimation in Large-Scale RFID Systems

2020 ◽  
pp. 1-1
Author(s):  
Zhong Xi ◽  
Xuan Liu ◽  
Juan Luo ◽  
Shigeng Zhang ◽  
Song Guo
Keyword(s):  
Large Scale ◽  
Rfid Systems
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