Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Performance analysis of the IEEE 802.16e power management for the initiations of awakening

To increase battery life in IEEE 802.16e systems, it is essential to efficiently manage energy in mobile stations. The sleep-mode operation in power management helps to increase the life of a station by saving energy consumption. In power management, there are two important performance metrics energy consumption and the response delay of awakening medium access control (MAC) service data unit (SDU). While in a base station (BS) initiation of awakening, the two performance metrics should be simultaneously considered, in a mobile subscriber station (MSS) initiation of awakening, the response delay is not considered because it is self-operational. There performance metrics are affected by the minimum sleep interval (Tmin), the maximum sleep interval (Tmax), and the average interarrival time of awakening MAC SDUs (TI) during sleep-mode operation. Therefore, it is imperative to evaluate the two initiations of awakening depending on TI. To reach a fuller understanding of the performance, this paper shows an analytical mode and simulations results for the standard sleep-mode operation in the IEEE 802.16e MAC.     

Maximum likelihood estimation for continuous-time autoregressive models by relaxation on residual variances ratio parameters

In this paper we derive an explicit expression for the log likelihood function of a continuous-time autoregressive model. Then, using earlier results relating the autoregressive coefficients to the set of positive parameters called residual variances ratios, we develop an iterative algorithm for computing the maximum likelihood estimator of the model, similar to one in the discrete-time case. A simple noniterative estimation method, which can be used to produce an initial estimate for the algorithm, is also proposed.     

Improved fracture toughness and fatigue life of carbon fiber reinforced epoxy composite due to incorporation of rubber nanoparticles

In this study, core–shell rubber (CSR) nanoparticles with approximate particle size of 35 nm were used as a modifier for the epoxy polymer. The effects of various CSR contents in the epoxy matrix on mode I interlaminar fracture toughness, tensile strength, and fatigue life of the carbon fabric reinforced epoxy (CF/EP) composites were investigated. The experimental results showed that the mode I interlaminar fracture toughness at crack initiation and propagation significantly improved by 71.21 and 58.47 %, respectively, when 8.0 wt% CSR was dispersed in the epoxy matrix. The fatigue life of the modified CF/EP composites at all of CSR contents dramatically increased 75–100 times longer than that of the unmodified CF/EP composites at high cycle fatigue while tensile strength slightly increased by about 10 %. Field emission scanning electron microcopy (FESEM) observations of the fracture surfaces were conducted to explain failure mechanisms of CSR addition to the CF/EP composites. The evidences of the rubber nanoparticle debonding, plastic void growth, and microshear banding were credited for delaying the onset of matrix crack, and reducing the crack growth rate, as a result, attributed to increase in the mechanical properties of the CF/EP composites.     

Reconstructing surfaces by volumetric regularization using radial basis functions

We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to vision-based data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data, (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data, and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

Plasma deposition of GaAs

The deposition of an amorphous film of GaAs with a low frequency and low power glow discharge is described. The structure and composition of the film were determined by IR, secondary ion mass spectrometry, electron paramagnetic resonance and electron diffraction measurements. First results for the dark conductivity and the optical absorption are reported.     

A printed circuit board capacitive sensor for air bubble inside fluidic flow detection

This paper presents a three-electrode capacitive fluidic sensor for detecting an air bubble inside a fluidic channel such as blood vessels, oil or medical liquid channels. The capacitor is designed and fabricated based on a printed circuit board (PCB). The electrodes are fabricated by using copper via structure through top to bottom surface of the PCB. A plastic pipe is layout through the capacitive sensor and perpendicular to the PCB surface. Capacitance of sensor changes when an air bubble inside fluidic flow cross the sensor. The capacitance change can be monitored by using a differential capacitive amplifier, a lock-in amplifier, filter and an NI acquisition card. Signal is processed and calculated on a computer. Air bubble inside the liquid flow are detected by monitor the unbalance signal between the three electrode potential voltages. Output voltage depends on the volume of the air bubble due to dielectric change between capacitor’s electrodes. Output voltage is up to 53 mV when an 2.28 mm³ air bubble crosses the sensing channel. Air bubble velocity can be estimated based on the output pulse signal. This proposed fluidic sensor can be used for void fraction detection in medical devices and systems; fluidic characterization; and water–gas, oil–water and oil–water–gas multiphase flows in petroleum technology. That structure also can apply to the micro-size for detecting in microfluidic to monitor and control changes in microfluidic channels.     

Synthesis, characterization and electrochemical properties of 4.8 V LiNi0.5Mn1.5O4 cathode material in lithium-ion batteries

In this work the synthesis of a nickel doped cubic manganese spinel has been studied for application as cathode material in secondary lithium batteries. Six different experimental approaches have been tested in order to carry out a screening of the various possible synthetic routes. The used synthetic strategies were wet chemistry (WC), solid state (SS), combustion synthesis (CS), cellulose-based sol-gel synthesis (SG-C), ascorbic acid-based sol-gel synthesis (SG-AA) and resorcinol/formaldehyde-based sol-gel synthesis (SG-RF). The goal of our study is to obtain insights about how the synthesis conditions can be modified in order to achieve a material with improved electrochemical performances in such devices, especially in high current operating regimes. The synthesized materials have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), atomic absorption, inductively coupled plasma (ICP-MS) atomic emission spectroscopy, surface area measurements and tested as high voltage cathodes in Li-ion electrochemical devices.