Abstract In educational or business environments, we often have a paperless meeting by using tablet devices. The meeting participants need to log into a meeting server of the meeting by using a token, a keyword to log into a meeting, defined for each meeting. Nevertheless, the meeting can be immediately held if the tablets ready for use by reducing the... In educational or business environments, we often have a paperless meeting by using tablet devices. The meeting participants need to log into a meeting server of the meeting by using a token, a keyword to log into a meeting, defined for each meeting. Nevertheless, the meeting can be immediately held if the tablets ready for use by reducing the costs of logging in of each participant. To reduce the costs, we proposed a new grouping method of tablet devices. The grouping method uses proximity information from a Bluetooth beacon to recognize grouped devices that are near each other. Moreover, the method can solve a problem that each device can be an obstacle attenuating signals for grouping. By using the method, working efficiency with tablet devices can be improved because the attendees do not need to operate each device individually to group them. Therefore, the devices can be grouped just by gathering them in a specific nearby location. We observed experimentally that the mechanism can resolve problems of reduced the attenuation of Bluetooth signals received by all immediate beacon receivers. Read More Read Less

Abstract In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors. We develop a mobile application that integrates data collection, training and recognition, feedback monitoring. This system allows user smartphones are randomly placed in any position and at any direction. In the proposed framework, Fast Fourier Transform (FFT) is used to extract a set... In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors. We develop a mobile application that integrates data collection, training and recognition, feedback monitoring. This system allows user smartphones are randomly placed in any position and at any direction. In the proposed framework, Fast Fourier Transform (FFT) is used to extract a set of features from sensor data. Then, we deploy Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) classification algorithms for recognizing a set of user activities. Our framework dynamically takes into account real-time user feedbacks to increase prediction accuracy. Our framework will be able to apply for intelligent mobile applications. A number of experiments were carried out to show the high accuracy of the proposed framework for detecting user activity when walking or driving a motorbike. Read More Read Less

Abstract In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the... In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the elimination is discarding the candidates which have accumulated squared Euclidean distances larger than calculated threshold. The simulation results show that the proposed QRD-M has same bit error rate (BER) performance with lower complexity than the conventional QRD-M. Read More Read Less

Abstract In educational or business environments, we often have a paperless meeting by using tablet devices. The meeting participants need to log into a meeting server of the meeting by using a token, a keyword to log into a meeting, defined for each meeting. Nevertheless, the meeting can be immediately held if the tablets ready for use by reducing the costs... In educational or business environments, we often have a paperless meeting by using tablet devices. The meeting participants need to log into a meeting server of the meeting by using a token, a keyword to log into a meeting, defined for each meeting. Nevertheless, the meeting can be immediately held if the tablets ready for use by reducing the costs of logging in of each participant. To reduce the costs, we proposed a new grouping method of tablet devices. The grouping method uses proximity information from a Bluetooth beacon to recognize grouped devices that are near each other. Moreover, the method can solve a problem that each device can be an obstacle attenuating signals for grouping. By using the method, working efficiency with tablet devices can be improved because the attendees do not need to operate each device individually to group them. Therefore, the devices can be grouped just by gathering them in a specific nearby location. We observed experimentally that the mechanism can resolve problems of reduced the attenuation of Bluetooth signals received by all immediate beacon receivers. Read More Read Less

Abstract In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of... In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the elimination is discarding the candidates which have accumulated squared Euclidean distances larger than calculated threshold. The simulation results show that the proposed QRD-M has same bit error rate (BER) performance with lower complexity than the conventional QRD-M. Read More Read Less

Abstract In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors. We develop a mobile application that integrates data collection, training and recognition, feedback monitoring. This system allows user smartphones are randomly placed in any position and at any direction. In the proposed framework, Fast Fourier Transform (FFT) is used to extract a set... In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors. We develop a mobile application that integrates data collection, training and recognition, feedback monitoring. This system allows user smartphones are randomly placed in any position and at any direction. In the proposed framework, Fast Fourier Transform (FFT) is used to extract a set of features from sensor data. Then, we deploy Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) classification algorithms for recognizing a set of user activities. Our framework dynamically takes into account real-time user feedbacks to increase prediction accuracy. Our framework will be able to apply for intelligent mobile applications. A number of experiments were carried out to show the high accuracy of the proposed framework for detecting user activity when walking or driving a motorbike. Read More Read Less