Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

On the perspective transformation for efficient relay placement in wireless multicast networks

This letter investigates the relay placement problem in wireless multicast networks consisting of multiple sources, relays, and destinations. The data transmission from the sources to the destinations is carried out via the relays employing physical-layer network coding technique. Hybrid automatic repeat request protocol with incremental redundancy is applied for reliable communication. In particular, considering a general setting of nodes in irregularly shaped network, an efficient relay placement algorithm is proposed based on perspective transformation technique to find optimal relay positions for minimizing either the total energy consumption or the total delay in the whole network. The proposed algorithm not only helps reduce the relay searching complexity but also facilitates the relay placement for optimizing networks of any shape

Differential spatial modulation for high-rate transmission systems

This paper introduces a new differential spatial modulation (DSM) scheme which subsumes both the previously introduced DSM and high-rate spatial modulation (HR-SM) for wireless multiple input multiple output (MIMO) transmission. By combining the codeword design method of the HR-SM scheme with the encoding method of the DSM scheme, we develop a high-rate differential spatial modulation (HR-DSM) scheme equipped with an arbitrary number of transmit antennas that requires channel state information (CSI) neither at the transmitter nor at the receiver. The proposed approach can be applied to any equal energy signal constellations. The bit error rate (BER) performance of the proposed HR-DSM schemes is evaluated by using both theoretical upper bound and computer simulations. It is shown that for the same spectral efficiency and antenna configuration, the proposed HR-DSM outperforms the DSM in terms of bit error rate (BER) performance

Fractional Chern Insulators from the nth Root of Bandstructure

We provide a parton construction of wavefunctions and effective field theories for fractional Chern insulators. We also analyze a strong coupling expansion in lattice gauge theory that enables us to reliably map the parton gauge theory onto the microsopic Hamiltonian. We show that this strong coupling expansion is useful because of a special hierarchy of energy scales in fractional quantum Hall physics. Our procedure is illustrated using the Hofstadter model and then applied to bosons at 1/2 filling and fermions at 1/3 filling in a checkerboard lattice model recently studied numerically. Because our construction provides a more or less unique mapping from microscopic model to effective parton description, we obtain wavefunctions in the same phase as the observed fractional Chern insulators without tuning any continuous parameters.Comment: 9+3 pages, 6 figures; v2: added refs, amplified discussion of deconfinement, improved discussion of translation invarianc

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions

Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

International audienceRecent years have witnessed the rapid development of human activity recognition (HAR) based on werable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

Emergence of geometrical optical nonlinearities in photonic crystal fiber nanowires

We demonstrate analytically and numerically that a subwavelength-core dielectric photonic nanowire embedded in a properly designed photonic crystal fiber cladding shows evidence of a previously unknown kind of nonlinearity (the magnitude of which is strongly dependent on the waveguide parameters) which acts on solitons so as to considerably reduce their Raman self-frequency shift. An explanation of the phenomenon in terms of indirect pulse negative chirping and broadening is given by using the moment method. Our conclusions are supported by detailed numerical simulations.Comment: 5 pages, 3 figure

Enhancement of Friction between Carbon Nanotubes: An Efficient Strategy to Strengthen Fibers

Interfacial friction plays a crucial role in the mechanical properties of carbon nanotube based fibers, composites, and devices. Here we use molecular dynamics simulation to investigate the pressure effect on the friction within carbon nanotube bundles. It reveals that the intertube frictional force can be increased by a factor of 1.5 ~ 4, depending on tube chirality and radius, when all tubes collapse above a critical pressure and when the bundle remains collapsed with unloading down to atmospheric pressure. Furthermore, the overall cross-sectional area also decreases significantly for the collapsed structure, making the bundle stronger. Our study suggests a new and efficient way to reinforce nanotube fibers, possibly stronger than carbon fibers, for usage at ambient conditions.Comment: revtex, 5 pages, accepted by ACS Nano 10 Dec 200

Industrial Internet of Things, Big Data, and Artificial Intelligence in the Smart Factory: a survey and perspective

International audienceThanks to the rapid development and applications of advanced technologies, we are experiencing the fourth industrial revolution, or Industry 4.0, which is a revolution towards smart manufacturing. The wide use of cyber physical systems and Internet of Things leads to the era of Big Data in industrial manufacturing. Artificial Intelligence algorithms emerge as powerful analytics tools to process and analyze the Big Data. These advanced technologies result in the introduction of a new concept in the Industry 4.0: the smart Factory. In order to fully understand this new concept in the context of the Industry 4.0, this paper provides a survey on the key components of a smart factory and the link between them, including the Industrial Internet of Things, Big Data and Artificial Intelligence. Several studies and techniques that are used to enable smart manufacturing are reviewed. Finally, we discuss some perspectives for further researches

The First Data Release of the KODIAQ Survey

We present and make publicly available the first data release (DR1) of the Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ) survey. The KODIAQ survey is aimed at studying galactic and circumgalactic gas in absorption at high-redshift, with a focus on highly-ionized gas traced by OVI, using the HIRES spectrograph on the Keck-I telescope. KODIAQ DR1 consists of a fully-reduced sample of 170 quasars at 0.29 < z_em < 5.29 observed with HIRES at high resolution (36,000 <= R <= 103,000) between 2004 and 2012. DR1 contains 247 spectra available in continuum normalized form, representing a sum total exposure time of ~1.6 megaseconds. These co-added spectra arise from a total of 567 individual exposures of quasars taken from the Keck Observatory Archive (KOA) in raw form and uniformly processed using a HIRES data reduction package made available through the XIDL distribution. DR1 is publicly available to the community, housed as a higher level science product at the KOA. We will provide future data releases that make further QSOs, including those with pre-2004 observations taken with the previous-generation HIRES detectors.Comment: 14 pages, 9 figures, Submitted to AJ. All data products available at the Keck Observatory Archive beginning May 15, 2015. URL: https://koa.ipac.caltech.edu/applications/KODIA