IMPROVED INDOOR POSITIONING USING BLE DIFFERENTIAL DISTANCE CORRECTION AND PEDESTRIAN DEAD RECKONING

Recently, indoor positioning becomes a popular issue because of its corresponding location-aware applications. Owing to the limits of the sheltered signal of satellites in indoor environments, one of the alternative scheme is Bluetooth Low Energy (BLE) technology. BLE device broadcasts Received Signal Strength Indicator (RSSI) for distance estimation and further positioning. However, in the complex indoor environment, the reflection, fading, and multipath effect of BLE make the variable RSSI and may lead to poor quality of RSSI. In this study, the concept called Differential Distance Correction (DDC) is similar to the Differential Global Navigation Satellite System (DGNSS). This method can eliminate some residuals and further improve the results with the corrected distance. On the other hand, Pedestrian Dead Reckoning (PDR) is another common indoor positioning method. PDR can propagate the next position from the current position by the implemented of inertial sensors. Despite that, the error of inertial sensors would accumulate with time and walking distance, which position update is required for restraining the drift. Accordingly, the two indoor positioning methods have their strong and weak point. BLE-based positioning is absolute positioning, while PDR is relative positioning. This study proposes a concept that combines the two methods. The pedestrian receives the RSSI and records the information from inertial sensors simultaneously. Through the complementary of two methods, the positioning results would be improved from 29% to 66% according to different travelled distance.

Stochastic allocation strategy for irregular arrays based on geometric feature control

Irregularities in microphone distribution enrich the diversity of spatial differences to decorrelate interferences from the beamforming target. However, the large degrees of freedom of irregular placements make it difficult to analyse and optimize array performance. This article proposes fast and feasible optimal irregular array design methods with improved beamforming performance for human speech. Important geometric features are extracted to be used as the input vector of the neural network structure to determine the optimal irregular arrangements of sensors. In addition, a hyperbola design method is proposed to directly cluster microphones in the hyperbola areas to produce rich differential distance entropies and yield significant signal-to-noise ratio improvements. These methods can be easily applied to guide non-computer-aided optimal irregular array designs for human speech in acoustic scenes such as immersive cocktail party environments.

Combining Non-Uniform Time Slice and Finite Difference to Improve 3D Ghost Imaging

Three-dimensional ghost imaging (3DGI) using a detector is widely used in many applications. The performance of 3DGI based on a uniform time slice is difficult to improve because obtaining an accurate time-slice position remains a challenge. This paper reports a novel structure based on non-uniform time slice combined with finite difference. In this approach, finite difference is beneficial to improving sensitivity of zero crossing to accurately obtain the position of the target in the field of view. Simultaneously, non-uniform time slice is used to quickly obtain 3DGI on an interesting target. Results show that better performances of 3DGI are obtained by our proposed method compared to the traditional method. Moreover, the relation between time slice and the signal-noise-ratio of 3DGI is discussed, and the optimal differential distance is obtained, thus motivating the development of a high-performance 3DGI.

Diagnostics of low-capacity solar power station equipment with 2- and 3-valued logic

The paper outlines research issues relating to 2- and 3-valued logic diagnoses developed with the diagnostic system (DIA G 2) for the equipment installed at a low-capacity solar power station. The presentation is facilitated with an overview and technical description of the functional and diagnostic model of the low-power solar power station. A model of the low-power solar power station (the tested facility, a.k.a. the test object) was developed, from which a set of basic elements and a set of diagnostic outputs were determined and developed by the number of functional elements j of j. The work also provides a short description of the smart diagnostic system (DIA G 2) used for the tests shown herein. (DIA G 2) is a proprietary work. The diagnostic program of (DIA G 2) operates by comparing a set of actual diagnostic output vectors to their master vectors. The output of the comparison are elementary divergence metrics of the diagnostic output vectors determined by a neural network. The elementary divergence metrics include differential distance metrics which serve as the inputs for (DIA G 2) to deduct the state (condition) of the basic elements of the tested facility. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence.

Diagnostic testing the solar power equipment in logic 2- and 3- valuable

The article presents the problem of the study of developed diagnoses in logic2- and 3-valuable diagnostic system (DIAG 2) devices of the solar power. For this purpose, a functional-diagnostic model of solar power devices has been described. On the basis of the elaborated model of the investigated object, a set of basic elements and a set of diagnostic signals, that are generated by the j-th elements of the functional object, have been determined. Also, there was given a brief description of intelligent diagnostic (DIAG 2) system used for the study. The system (DIAG 2) is a proprietary development of the authors. Diagnostic software in (DIAG 2) system works on the principle of comparison of the set of vectors of diagnostic signals with their standard vectors. By comparing the signals, elementary metrics of vectors disparity of diagnostic signals are determined by the neural network. On the basis of the metrics of differential distance, the system inferences about the diagnosis on the state of elements of a basic object. Keywords: technical diagnostics, diagnostic reasoning, multivalent logic, artificial intelligence

DNA Bar-coding: A Novel Approach for Identifying an Individual Using Extended Levenshtein Distance Algorithm and STR analysis

DNA bar-coding is a technique that uses the short DNA nucleotide sequences from the standard genome of the species in order to find and group the species to which it belongs to. The species are identified by their DNA nucleotide sequences in the same way the items are recognized and billed in the supermarket using barcode scanner to scan the Universal Product Code of the items. Two items may look same to the untrained eye, but in both cases the barcodes are distinct. It was possible to create DNA-barcodes to characterize species by analysing DNA samples from fish, birds, mammals, plants, and invertebrates using Smith-waterman and Needleman-Wunsch algorithm. In this work we are creating human DNA barcode and implementing Extended Levenshtein distance algorithm along with STR analysis that uses less computation time compared to the previously used algorithms to measure the differential distance between the two DNA nucleotide sequences through which an individual can be identified.

DNA Bar-coding: A Novel Approach for Identifying an Individual Using Extended Levenshtein Distance Algorithm and STR analysis

DNA bar-coding is a technique that uses the short DNA nucleotide sequences from the standard genome of the species in order to find and group the species to which it belongs to. The species are identified by their DNA nucleotide sequences in the same way the items are recognized and billed in the supermarket using barcode scanner to scan the Universal Product Code of the items. Two items may look same to the untrained eye, but in both cases the barcodes are distinct. It was possible to create DNA-barcodes to characterize species by analysing DNA samples from fish, birds, mammals, plants, and invertebrates using Smith-waterman and Needleman-Wunsch algorithm. In this work we are creating human DNA barcode and implementing Extended Levenshtein distance algorithm along with STR analysis that uses less computation time compared to the previously used algorithms to measure the differential distance between the two DNA nucleotide sequences through which an individual can be identified.