Uniform error estimates for nonequispaced fast Fourier transforms

In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. So far, various window functions have been used and new window functions have recently been proposed. We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter.

Continuous window functions for NFFT

In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. Here, we consider the continuous Kaiser–Bessel, continuous exp-type, sinh-type, and continuous cosh-type window functions with the same support and same shape parameter. We present novel explicit error estimates for NFFT with such a window function and derive rules for the optimal choice of the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter. For the considered continuous window functions, the error constants have an exponential decay with respect to the truncation parameter.

GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

Probabilistic Approach to Understand Errors in Sequencing and its based Applications

Next Generation Sequencing has been applied in many areas of biology, including quantification of gene expression, Genome-Wide Association Study (GWAS), gene finding, Motif discovery and much more. Due to the vast area of application and importance in key findings in the field, massive genomic data is being generated using high throughput sequencing. Therefore, sequencing quality also needs to be evaluated in light of different applications. In order to develop effective diagnostic and therapeutic approaches, we need to accurately characterize and identify sequencing errors and distinguish these errors from their true genetic variant in sequencing, i.e. misreads follow a binomial distribution and it further can be approximated to the Poisson process for longer sequences. However, the insertion and deletion rates are 1000 times lower than substitution error rates and, therefore, less significant. The model assumes that error arrival at a position is not dependent on an error at other positions. Furthermore, errors in sequences can cause an error in studies based on multiple sequences and they also follow Binomial – Poisson Distribution (for example – Alignment is a merging of two Binomial processes for short sequences and it further can be approximated to Poisson for long sequences (for example – genomic sequence). It provides a systematic way to evaluate the accuracy in sequencing-based applications. Many error suppressing algorithms or techniques are there, and our Binomial Poisson model can provide a further systematic understanding of error behavior in short sequences so that more techniques for error removal can be developed with much efficient suppression rates.

Application of Structural Equation Modeling (SEM) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Assessment of Safety Culture: An Integrated Modeling Approach

The primary purpose of this study was to apply structural equation modeling (SEM) integrated with an adaptive neuro-fuzzy inference system (ANFIS) approach to model the safety culture of the petrochemical industry of Japan. Workers from five companies located in the Chugoku region of Japan completed a paper-based survey distributed by email. SEM and ANFIS methods were integrated in order to identify and model the important factors of the safety culture. The results of SEM indicate that employee attitudes toward safety, coworker’s support, work pressure, and plant safety management systems were significant factors influencing violation behavior, personnel safety motivation, and personnel error behavior. Furthermore, the application of the ANFIS modeling approach showed that employees’ attitude was the most critical predictor of violation behavior and personnel error behavior, while coworkers support was the most critical predictor in modeling personnel safety motivation.