scholarly journals An Estimation of QoS for Classified Based Approach and Nonclassified Based Approach of Wireless Agriculture Monitoring Network Using a Network Model

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ismail Ahmedy ◽  
Hisham A. Shehadeh ◽  
Mohd Yamani Idna Idris
Keyword(s):  
Large Scale ◽  
Monitoring Network ◽  
Wireless Sensor ◽  
Dense Network ◽  
High Quality ◽  
Multihop Network ◽  
Simulation Results ◽  
Test Scenarios ◽  
Higher Sensitivity

Wireless Sensor Network (WSN) can facilitate the process of monitoring the crops through agriculture monitoring network. However, it is challenging to implement the agriculture monitoring network in large scale and large distributed area. Typically, a large and dense network as a form of multihop network is used to establish communication between source and destination. This network continuously monitors the crops without sensitivity classification that can lead to message collision and packets drop. Retransmissions of drop messages can increase the energy consumption and delay. Therefore, to ensure a high quality of service (QoS), we propose an agriculture monitoring network that monitors the crops based on their sensitivity conditions wherein the crops with higher sensitivity are monitored constantly, while less sensitive crops are monitored occasionally. This approach selects a set of nodes rather than utilizing all the nodes in the network which reduces the power consumption in each node and network delay. The QoS of the proposed classified based approach is compared with the nonclassified approach in two scenarios; the backoff periods are changed in the first scenario while the numbers of nodes are changed in the second scenario. The simulation results demonstrate that the proposed approach outperforms the nonclassified approach on different test scenarios.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

Novel Dielectric Stack Actuators for Dynamic Applications

2012 ◽  
Author(s):  
Sven Herold ◽  
William Kaal ◽  
Tobias Melz
Keyword(s):  
Simulation Model ◽  
Surface Resistance ◽  
Design Concept ◽  
Metal Electrodes ◽  
High Quality ◽  
Complex Material ◽  
Simulation Results ◽  
Actuator Design ◽  
Load Conditions

In order to realize dielectric elastomer stack actuators suitable for dynamic applications a new actuator design with rigid, perforated electrodes is developed. The low surface resistance of the metal electrodes predestines this concept for dynamic applications where higher currents are present. Detailed numerical analyses are performed to show the potential of this approach, to study the complex material deformation and to optimize the aperture geometry. A multilayer stack actuator is then manufactured and characterized experimentally under various load conditions to gain suitable parameters for a parametrized model. It is subsequently used to attenuate vibrations of a truss structure. By careful adjusting the parameters it functions both as passive absober and as actuator. A comparison of experimental and simulation results proves the high quality of the simulation model. The work shows the great potential of the new design concept for future applications especially in the field of smart structures.

scholarly journals Scientific and practical fundamentals of growing cabbage (Brassica capitata lizg.) in Uzbekistan

2021 ◽  
Vol 939 (1) ◽  
pp. 012044
Author(s):  
A J Shokirov ◽  
S S Lapasov ◽  
K J Shokirov
Keyword(s):  
Large Scale ◽  
Scientific Research ◽  
Mineral Fertilizers ◽  
Biological Productivity ◽  
High Quality ◽  
Planting Time ◽  
Yield And Quality ◽  
White Cabbage ◽  
Urgent Task

Abstract At present, scientific research is underway to further develop vegetable growing in the secondary crop, in particular to further increase the yield and quality of white cabbage, to select a system of planting time-sowing scheme that maximizes the biological productivity of varieties, and to apply the most optimal standards of fertilization and irrigation. In this regard, the urgent task remains to determine the optimal varieties of cabbage that can be grown in repeated crops, their optimal planting scheme, timing, development and implementation of optimal standards for each variety of mineral fertilizers and irrigation, and its solution is large-scale throughout the country. Besides that a number of problematic issues are addressed, which could allow to get high and high-quality harvest of white cabbage in repeated sowing in grain-free areas.

scholarly journals Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing

2017 ◽  
Author(s):  
Wei Wang ◽  
Xiang-Yu Guo ◽  
Shao-Yuan Li ◽  
Yuan Jiang ◽  
Zhi-Hua Zhou
Keyword(s):  
Large Scale ◽  
Experimental Results ◽  
Training Set ◽  
High Quality ◽  
Quality Label ◽  
Large Scale Data ◽  
Voluntary Workers ◽  
Scale Data

Crowdsourcing systems make it possible to hire voluntary workers to label large-scale data by offering them small monetary payments. Usually, the taskmaster requires to collect high-quality labels, while the quality of labels obtained from the crowd may not satisfy this requirement. In this paper, we study the problem of obtaining high-quality labels from the crowd and present an approach of learning the difficulty of items in crowdsourcing, in which we construct a small training set of items with estimated difficulty and then learn a model to predict the difficulty of future items. With the predicted difficulty, we can distinguish between easy and hard items to obtain high-quality labels. For easy items, the quality of their labels inferred from the crowd could be high enough to satisfy the requirement; while for hard items, the crowd could not provide high-quality labels, it is better to choose a more knowledgable crowd or employ specialized workers to label them. The experimental results demonstrate that the proposed approach by learning to distinguish between easy and hard items can significantly improve the label quality.

Data Science and Knowledge Analytic Contexts on IoE Data for E-BI Application Case

2019 ◽  
pp. 100-126
Author(s):  
Nilamadhab Mishra
Keyword(s):  
Large Scale ◽  
Data Science ◽  
Low Cost ◽  
Edge Computing ◽  
The Other ◽  
High Quality ◽  
Main Research ◽  
Cognitive Values ◽  
Research Challenge

The progressive data science and knowledge analytic tasks are gaining popularity across various intellectual applications. The main research challenge is to obtain insight from large-scale IoE data that can be used to produce cognitive actuations for the applications. The time to insight is very slow, quality of insight is poor, and cost of insight is high; on the other hand, the intellectual applications require low cost, high quality, and real-time frameworks and algorithms to massively transform their data into cognitive values. In this chapter, the author would like to discuss the overall data science and knowledge analytic contexts on IoE data that are generated from smart edge computing devices. In an IoE-driven e-BI application, the e-consumers are using the smart edge computing devices from which a huge volume of IoE data are generated, and this creates research challenges to traditional data science and knowledge analytic mechanisms. The consumer-end IoE data are considered the potential sources to massively turn into the e-business goldmines.

Performance of Wireless Sensor Networks for Different Mobile Event Path Scenarios

2012 ◽  
pp. 55-68
Author(s):  
Tao Yang ◽  
Gjergji Mino ◽  
Leonard Barolli ◽  
Makoto Ikeda ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Energy Characteristic ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Movement Path ◽  
Random Movement ◽  
Simulation Results

In this paper, the authors investigate how the sensor network performs when the event moves with special movement path. Simulation results are compared with four scenarios: when the event is stationary, moving randomly, moving with simple 4 path, and boids path. The simulation results show that for the case when the event is moving randomly, the performance is the worst in the four scenarios. The characteristic of goodput decreases with the increase of number of sensor nodes. In the case of the boids model, the goodput is unstable when the is lower than 10 pps. The consumed energy characteristic increases with the increase of Simulation results show that the consumed energy of random movement is the worst among the four scenarios. The consumed energy of boids model is the lowest in four cases. This shows that the event movement with boids model can decrease the consumed energy in large scale WSNs.

scholarly journals Application and Design of Patient Temperature Acquisition System Based on Wireless Sensor Network

2017 ◽  
Vol 13 (05) ◽  
pp. 18
Author(s):  
Huijie Ding
Keyword(s):  
Large Scale ◽  
Monitoring Network ◽  
Temperature Monitoring ◽  
Wireless Sensor ◽  
Large Hospital ◽  
Clinical Measurement ◽  
Number Of Patients ◽  
Abnormal Phenomenon ◽  
Time Acquisition ◽  
Abnormal Temperature

The purpose of this study is to achieve large-scale real-time acquisition and monitoring of the patient’s body temperature, for noticing abnormal phenomenon of temperature in time. In the ward, arrange ZigBee wireless temperature monitoring network, realize the ward patients temperature measurement and gathering of temperature related clinical data, achieve the temperature monitoring network through the serial RS232 and PC communication, and PC receives, displays, and processes the data through the serial port. The results showed that the system can also realize the acquisition, transmission and monitoring of a plurality of temperature signals, and send out the alarm in abnormal temperature. As a result, the system can be widely used in clinical measurement, suitable for popularization in large hospital with a large number of patients

scholarly journals Node Deployment in Farmland Wireless Sensor Networks based on Renewable Energy

2016 ◽  
Vol 12 (10) ◽  
pp. 62
Author(s):  
Jianwei Wu ◽  
Yisheng Miao
Keyword(s):  
Wireless Sensor Networks ◽  
Power Consumption ◽  
Large Scale ◽  
Hot Spot ◽  
Wireless Sensor ◽  
Low Power Consumption ◽  
Network Coverage ◽  
Font Size ◽  
Deployment Strategy ◽  
Simulation Results

<p><span style="font-size: small;"><span style="font-family: Times New Roman;">Wireless sensor network (WSN) plays an important role in the large scale farmland environmental monitoring. The complex farmland environment has a great impact on the WSN performance. Extremely low power consumption of WSN is required because of the long monitoring period and limited energy. In the considering of network coverage, connectivity, organization and power consumption, this paper proposes a new deployment strategy in the consideration of solar power nodes. A mixed deployment method combining with structure and random filling deployment is used. The hot-spot nodes in the network are replaced by solar nodes in order to get a longer lifetime. The simulation results show that the new method has a better performance than the traditional algorithms.</span></span></p>

scholarly journals A Time Difference of Arrival-based Localization Algorithm for Wireless Sensor Networks

2016 ◽  
Vol 12 (11) ◽  
pp. 80 ◽  
Author(s):  
Songbo Ji
Keyword(s):  
Wireless Sensor Networks ◽  
Electromagnetic Wave ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Difference ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Time Difference Of Arrival ◽  
Positioning Accuracy ◽  
Simulation Results

<p class="Abstract"><span lang="EN-US">Aimed at solving the problem of local divergence and low data accuracy, this paper introduces a new Time Difference of Arrival(TDOA)-based localization algorithm (TBL) for the large-scale, high-density wireless sensor networks which are designed for real-time surveillance and unexpected incidents management. In particular, several means to improve the accuracy of distance measurement are investigated, and the TDOA method, based on the sound wave and electromagnetic wave to locate in the large-scale WSN, is discussed. Also, the well-designed circular location process has the advantage of better positioning accuracy and coverage percentage. Simulation results have confirmed the effectiveness of the formed TBL algorithm.</span></p>

scholarly journals Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset

2020 ◽  
Vol 34 (04) ◽  
pp. 6811-6820
Author(s):  
Ruohan Zhang ◽  
Calen Walshe ◽  
Zhuode Liu ◽  
Lin Guan ◽  
Karl Muller ◽  
...  
Keyword(s):  
Decision Making ◽  
Eye Movements ◽  
Visual Attention ◽  
Large Scale ◽  
Imitation Learning ◽  
High Quality ◽  
Human Decision ◽  
Public Datasets ◽  
Game Performance

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.

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