Synthesis of 1,2-dimethyl-6-(2' -hydroxyisopropyl)-7 -methoxynaphthalene, an isomer of emmotin-G methyl ether

857-86

Senslide: a distributed landslide prediction system

We describe the design, implementation, and current status of Senslide, a distributed sensor system aimed at predicting landslides in the hilly regions of western India. Landslides in this region occur during the monsoon rains and cause significant damage to property and lives. Unlike existing solutions that detect landslides in this region, our goal is to predict them before they occur. Also, unlike previous efforts that use a few but expensive sensors to measure slope stability, our solution uses a large number of inexpensive sensor nodes inter-connected by a wireless network. Our system software is designed to tolerate the increased failures such inexpensive components may entail. We have implemented our design in the small on a laboratory testbed of 65 sensor nodes, and present results from that testbed as well as simulation results for larger systems up to 400 sensor nodes. Our results are sufficiently encouraging that we intend to do a field test of the system during the monsoon season in India

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification

Comparative study of Heck reaction under thermal and microwave conditions

2235-2237Comparative study of Heck reaction of various substrates under thermal and microwave irradiation reveals advantage of microwave synthesis

Deuterium labelling studies with aliphatic amines

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A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. In this paper, we provide an effective solution to the tracking of multiple single-pixel maneuvering targets in a sequence of infrared images by developing an algorithm that combines a sequential probabilistic multiple hypothesis tracking (PMHT) and interacting multiple model (IMM). We explicitly model maneuver as a change in the target's motion model and demonstrate its effectiveness in our tracking application discussed in this paper. We show that inclusion of IMM enables tracking of any arbitrary trajectory in a sequence of infrared images without any a priori special information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the observation origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses observation association as missing data

TRACKING MULTIPLE MANEUVERING POINT TARGETS USING MULTIPLE FILTER BANK IN INFRARED IMAGE SEQUENCE

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no apriori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion ( ¦ pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any apriori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) Interacting Multiple Model (IMM) filtering which required the maneuver parameters, and (ii) Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for nonlinear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target. 1