WSNs (wireless sensor networks) are networks that hundreds or thousands of nodes poured in a field and nodes try to send sensed event to base station (BS). In many cases, the BS isn't in the field and is so far away from nodes. Energy efficiency is one of the major concerns in wireless sensor networks since it impacts the network lifetime. So instead of transmitting directly to BS, in hierarchical network, we choose a cluster head (CH) to send aggregated data from neighbors to BS. In this paper we investigate a new threshold assignment for LEACH and xLEACH that improves energy consumption. We insert the distances of nodes from BS in threshold assignment in order to unbalance the CH selection to reduce energy consumption in the network.
Abstract. Policymaking and planning agricultural improvement require accurate and timely information and statistics. In Iran, collecting and acquiring agricultural statistics is often done in the traditional methods. Related studies have proved that these methods mostly contain some mistakes. Multi-temporal acquisition strategies of remotely sensed data provide an opportunity to improve rice monitoring and mapping. Studying and monitoring rice paddies in vast areas is limited by the presence of cloud cover, the spatial and temporal resolution of optical sensors, and the lack of open access or systematic Radar data. Sentinel-1 satellite data, which are free to access and has a high quality of spatial and temporal resolution, can provide a great opportunity for monitoring crop products, especially rice. In this study, Sigma Nought, Gamma Nought and Beta Nought time series of Sentinel-1 data in VV, VH and VV+VH polarizations were employed for extracting areas under rice cultivation in the region of Mazandaran province, Iran. These satellite data are taken regularly every 12 days, according to the season of the region, from March 21st to September 22nd of 2018. In this study, in order to specify the rice paddies area, several fieldworks were randomly carried out for two weeks, and field data were collected as well. Field data including rice paddies areas and non-rice areas were collected as ‘Test and Train data set’ and then the Random Forrest (RF) algorithm was carried out to determine the rice paddies area. The classification result was validated using test samples. The accuracy of all classifications results are over 80% and the best result is related to Sigma Nought and gamma Nought of VH polarization, with an accuracy of 91.37%. The results showed a high capability to evaluate and monitor rice production at moderate levels in a vast area which is regularly exposed to the cloud cover.
Rice is one of the most essential and strategic food sources globally. Accordingly, policymakers and planners often consider a special place in the agricultural economy and economic development for this essential commodity. Typically, a sample survey is carried out through field observations and farmers’ consultations to estimate annual rice yield. Studies show that these methods lead to many errors and are time-consuming and costly. Satellite remote sensing imagery is widely used in agriculture to provide timely, high-resolution data and analytical capabilities. Earth observations with high spatial and temporal resolution have provided an excellent opportunity for monitoring and mapping crop fields. This study used the time series of dual-pol synthetic aperture radar (SAR) images of Sentinel-1 and multispectral Sentinel-2 images from Sentinel-1 and Sentinel-2 ESA’s Copernicus program to extract rice cultivation areas in Mazandaran province in Iran. A novel multi-channel streams deep feature extraction method was proposed to simultaneously take advantage of SAR and optical imagery. The proposed framework extracts deep features from the time series of NDVI and original SAR images by first and second streams. In contrast, the third stream integrates them into multi-levels (shallow to deep high-level features); it extracts deep features from the channel attention module (CAM), and group dilated convolution. The efficiency of the proposed method was assessed on approximately 129,000 in-situ samples and compared to other state-of-the-art methods. The results showed that combining NDVI time series and SAR data can significantly improve rice-type mapping. Moreover, the proposed methods had high efficiency compared with other methods, with more than 97% overall accuracy. The performance of rice-type mapping based on only time-series SAR images was better than only time-series NDVI datasets. Moreover, the classification performance of the proposed framework in mapping the Shirodi rice type was better than that of the Tarom type.
Diesel-electric locomotives consume a significant amount of fuel in rail transportation systems. The power transmission system of these locomotives is similar to that of hybrid electric vehicles, so the available diesel-electric locomotives can be promoted to series hybrid locomotives by adding an energy storage source. In this study, the GM SD40-2 locomotive is considered as a case study and the series hybrid structure for this locomotive is designed and simulated by adding a lithium-ion battery pack. Additionally, control strategy plays an important role in reducing the amount of fuel consumed by hybrid electric vehicles. The fuzzy look-ahead control is applied as an online approach for fuel consumption reduction in railway transportation. A fuzzy controller modifies throttle position by accounting for the battery state of charge, the gradient and desired speed of the path ahead. The model developed in this study for train motion simulation considers the locomotive subsystems and satisfies the experimental fuel consumption data specified in the locomotive's catalog. A simulation of a freight train with the GM SD40-2 locomotive on a local track showed considerable improvement of fuel economy when using series hybrid structure in conjunction with our proposed algorithm for diesel-electric locomotives.
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