Research on 5G positioning technology for urban canyon

Aiming at the problem of poor positioning accuracy of GNSS for urban canyon environment, this paper proposes a TDOA/AOA combined positioning technology based on 5G.Firstly, the AOA estimation algorithm based on conventional beam forming is compared with that based on MUSIC algorithm. Then, the weighted matrix is smoothed forward and backward.Finally, according to the measurement data of AOA and TDOA, Chan and Taylor combined localization algorithm is used to obtain the estimated location of the user.

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.

Discriminate Supervised Weighted Scheme for the Classification of Time Series Signals

Innovations and applicability of time series data mining techniques have significantly increased the researchers' interest in the problem of time series classification. Several algorithms have been proposed for this purpose categorized under shapelet, interval, motif, and whole series-based techniques. Among this, the bag-of-words technique, an extensive application of the text mining approach, performs well due to its simplicity and effectiveness. To extend the efficiency of the bag-of-words technique, this paper proposes a discriminate supervised weighted scheme to identify the characteristic and representative pattern of a class for efficient classification. This paper uses a modified weighted matrix that discriminates the representative and non-representative pattern which enables the interpretability in classification. Experimentation has been carried out to compare the performance of the proposed technique with state-of-the-art techniques in terms of accuracy and statistical significance.

Improving Customers Satisfaction through Significance of Technical Attribute in QFD Studies

Purpose: Quality Function Deployment, (QFD) is a methodology which helps to satisfy customer requirements through the selection of appropriate Technical Attributes (TAs). The rationale of this article is to provide a method lending statistical support to the selection of TAs. The purpose is to determine the statistical significance of TAs through the derivation of associated significance (P) values. Design/Methodology/Approach: We demonstrate our methodology with reference to an original QFD case study aimed at improving the educational system in high schools in Pakistan; and then with five further published case studies obtained from literature. Mean weights of TAs are determined. Considering each TA mean weight to be a Test Statistic, a weighted matrix is generated from the VOCs’ importance ratings, and ratings in the relationship matrix. Finally using R, P-values for the means of original TAs are determined from the hypothetical population of means of TAs. Findings: Each TA’s P-value evaluates its significance/insignificance in terms of distance from the grand mean. P-values indirectly set the prioritization of TAs. Implications/Originality/Value: The novel aspect of this study is extension of mean weights of TAs, to also provide P-values for TAs. TAs with significant importance can be resolved on priority basis, while other can be fixed with appropriateness.

Design of a recommendation system based on the transition graph

A recommendation system has been built for a web resource’s users that applies statistics about user activities to provide recommendations. The purpose of the system operation is to provide recommendations in the form of an orderly sequence of HTML pages of the resource suggested for the user. The ranking procedure uses statistical information about user transitions between web resource pages. The web resource model is represented in the form of a web graph; the user behavior model is shown as a graph of transitions between resource pages. The web graph is represented by an adjacency matrix; for the transition graph, a weighted matrix of probabilities of transitions between the vertices of the graph has been constructed. It was taken into consideration that user transitions between pages of a web resource may involve entering a URL in the address bar of a browser or by clicking on a link in the current page. The user’s transition between vertices in a finite graph according to probabilities determined by the weight of the graph’s edges is represented by a homogeneous Markov chain and is considered a process of random walk on the graph with the possibility of moving to a random vertex. Random Walk with Restarts was used to rank web resource pages for a particular user. Numerical analysis has been performed for an actual online store website. The initial data on user sessions are divided into training and test samples. According to the training sample, a weighted matrix of the probability of user transitions between web resource pages was constructed. To assess the quality of the built recommendation system, the accuracy, completeness, and Half-life Utility metrics were used. On the elements of the test sample, the accuracy value of 65‒68 % was obtained, the optimal number of elements in the recommendation list was determined. The influence of model parameters on the quality of recommendation systems was investigated.

Process development options for electronic waste fractionation to achieve maximum material value recovery

Revised legislation and bans on imports of waste electrical and electronic equipment (WEEE) into many Asian countries for treatment are driving the need for more efficient WEEE fractionation in Europe by expanding the capacity of treatment plants and improving the percentage recovery of materials of economic value. Data from a key stakeholder survey and consultation are combined with the results of a detailed literature survey to provide weighted matrix input into multi-criteria decision analysis calculations to carry out the following tasks: (a) assess the relative importance of 12 process options against the 6 industry-derived in-process economic potential criteria, that is, increase in product quality, increase in recycling rate, increase in process capacity, decrease in labour costs, decrease in energy costs and decrease in disposal costs; and (b) rank 25 key technologies that have been selected as being the most likely to benefit the efficient sorting of WEEE. The results indicate that the first stage in the development of any total system to achieve maximum economic recovery of materials from WEEE has to be the selection and application of appropriate fractionation process technologies to concentrate valuable components such as critical metals into the smallest possible fractions to achieve their recovery while minimising the disposal costs of low-value products. The stakeholder-based study has determined the priority for viable technical process developments for efficient WEEE fractionation and highlighted the economic and technical improvements that have to be made in the treatment of WEEE.