Current Issue : July-September Volume : 2022 Issue Number : 3 Articles : 5 Articles
Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights....
Deep neural networks (DNNs) are widely used in many artificial intelligence applications; many specialized DNN-inference accelerators have been proposed. However, existing DNN accelerators rely heavily on certain types of DNN operations (such as Conv, FC, and ReLU, etc.), which are either less used or likely to become out of date in future, posing challenges of flexibility and compatibility to existing work. This paper designs a flexible DNN accelerator from a more generic perspective rather than speeding up certain types of DNN operations. Our proposed Nebula exploits the width property of DNNs and gains a significant improvement in system throughput and energy efficiency over multi-branch architectures. Nebula is a first-of-its-kind framework for multi-branch DNNs....
Maintaining the safe and efficient operation of network technology is an important development task of the computer industry. Topology constraint can optimize and combine the tracking results and select the target objects with better tracking performance to obtain the final tracking results and determine the target scale changes. Data mining technology can reduce the number of combinations to be detected, reduce the workload, and improve the timeliness and accuracy of the process of mining alarm association rules. Therefore, based on the summary and analysis of previous research results, this paper studied the network fault diagnosis of the embedded system method based on topology constraint and datamining. Firstly, a fault diagnosis topology model was established by constructing a topology search algorithm, which eliminated the filtering of association rules without topology relationship; the association rule-based data mining model was analyzed through the collection of network alarm data; the model algorithm was applied to the simulation experiment of network fault diagnosis of the embedded system and achieved good results. The results show that correcting rage of retrieval varies from 0.65 to 090 under different window sizes; the running time of the proposed method drops from 310 s to 35 s during 1–8 step/s of the sliding step, while the node degree ranges from 8 to 14 and diagnostic accuracy ranges from 0.97 to 0.94; the remaining alarm number increases from 0.5 to 3.5 threshold value, while the regular association number distributed in an interval of 40 to 140. The algorithm in this paper provides a reference for further research on network fault diagnosis of the embedded system....
In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle’s speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point....
With the popularity of Wing Chun film and television themes in recent years, Wing Chun has gradually received more and more attention, but the impact of Japanese punching boxing is rarely involved. The purpose of this article is to study the impact of Wing Chun Day Punch Boxing based on embedded microprocessor, to understand the actual effect of Wing Chun Day Punch Boxing through the analysis of the beat effect, and to provide reference and help for the research of traditional Chinese martial arts. In this paper, experimental methods and mathematical statistics combined with embedded microprocessors are used to research and analyze the punching effect of Wing Chun word punching. And it calculates the speed, angle, angular velocity, angular acceleration, muscle discharge sequence, and other data of various parts of the body during the process of Wing Chun force release to reveal the hitting effect of Wing Chun day word punching. Five Wing Chun practitioners with different training times are selected for Wing Chun day punch training, using engineering dummies for experiments, relying on acceleration sensors, position sensors, and force sensors. Starting from the direction of dynamics and kinematics, it collects the effective data of the Japanese word punch, then analyzes the experimental results through the embedded microprocessor, and obtains the hitting effect of the Wing Chun Japanese word punch. Experiments show that, through the analysis of the force angle and characteristics of the Japanese punch, the subjects’ elbow joint angle changes significantly when doing the Japanese punch. In the experiment on the force measurement engineering dummy, when the subjects punched, the measurement results of the vibration acceleration of the internal organs of different subjects were p > 0.01, and there was no significant difference. However, in the case of elbow joint 150° preparation, the midline punch is significantly larger than the punch directly in front of the shoulder, and the difference is statistically significant (significant level p < 0.01)....
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