Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Building Quantitative Cross-Cultural Databases From Ethnographic Records: Promise, Problems and Principles

Quantitative cross-cultural databases can help uncover structure and diversity across human populations. These databases have been constructed using a variety of methodologies and have been instrumental for building and testing theories in the social sciences. The processes and assumptions behind the construction of cross-cultural databases are not always openly discussed by creators or fully appreciated by their users. Here, we scrutinize the processes used to generate quantitative cross-cultural databases, from the point of ethnographic fieldwork to the processing of quantitative cross-cultural data. We outline challenges that arise at each stage of this process and discuss the strengths and limitations of how existing databases have handled these challenges. We suggest a host of best practices for cross-cultural database construction, and stress the importance of coding source meta-data and using this meta-data to identify and adjust for source biases. This paper explicitly discusses the processes, problems, and principles behind cross-cultural database construction, and ultimately seeks to promote rigorous cross-cultural comparative research.

Research on the Construction of Big Data Classification System Based on Distributed Data Flow

In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

Analysis of the Development Trend of Clinical Research Nurses in China Based On CiteSpace

Objective To reveal the development characteristics and trends of clinical research nurses in China and provide a reference for the training and employment of nursing talents. Methods Literature about clinical research nurses published from the year of database construction to 2020 were searched through the CNKI, Wanfang, Chinese Biomedical Literature (CBM) and Weipu (VIP) databases, and CiteSpace software was used to conduct a multidimensional analysis of the included literature. Results A total of 3,735 pieces of literature were retrieved, and after deduplication and screening, 199 pieces of literature were finally retained for this study. The practice and exploration of CRNs were regionalised, with varying degrees of development, and CRNs were at the forefront of development in oncology specialties. Conclusion It is important to continue to expand the breadth and depth of researchto promote the continuous development of China’s medical and health care to align with international standards.

Research on Similar Animal Classification Based on CNN Algorithm

Animal image classification with CNN (convolutional neural network) is commonly investigated in aera of image recogniation and classification, but major studies focus on species pictures classification with obvious distinctions. For example, CNN is usually employed to distinghish images between dogs and cats. This article puts the effort on similar animal images classification by applying simple 2D CNN via python. It focus on the binary classification for snub-nosed monkeys and normal monkeys. This distinguishment is hard to be done manually in a short time. For constructing complete convolutional neural network, some preparations are done in advance, such as the database construction and preprocess. The database is constructed by python crawler (downloading from google images), with 800 and 200 images for each class respectively as train data and test data. The pre-work includes image resizing, decoding and standardization. After that, the model is trained and then tested for verifying the model reliability. The training accuracy is 96.67% without any abnormality. On the basis of successful training, the test accuracy almost coincides with train accuracy in each 50 generations and plots in a graph. It indicates similar trends and results for them in the whole process. Because of this, CNN model in the study can help people identify rare animals in time and then people can effectively protect them. Therefore, CNN will be helpful in field of animal conservation, especially for rare species.

Increasing the power of interpretation for soil metaproteomics data

Background Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples. Results The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases. Conclusions A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.

Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets

Mapping metagenome reads to reference databases is the standard approach for assessing microbial taxonomic and functional diversity from metagenomic data. However, public reference databases often lack recently generated genomic data such as metagenome-assembled genomes (MAGs), which can limit the sensitivity of read-mapping approaches. We previously developed the Struo pipeline in order to provide a straight-forward method for constructing custom databases; however, the pipeline does not scale well enough to cope with the ever-increasing number of publicly available microbial genomes. Moreover, the pipeline does not allow for efficient database updating as new data are generated. To address these issues, we developed Struo2, which is >3.5 fold faster than Struo at database generation and can also efficiently update existing databases. We also provide custom Kraken2, Bracken, and HUMAnN3 databases that can be easily updated with new genomes and/or individual gene sequences. Efficient database updating, coupled with our pre-generated databases, enables “assembly-enhanced” profiling, which increases database comprehensiveness via inclusion of native genomic content. Inclusion of newly generated genomic content can greatly increase database comprehensiveness, especially for understudied biomes, which will enable more accurate assessments of microbiome diversity.