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Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor...
The study of network features is an important analysis method for the social networks, and prediction of network features is a research problem with many applications, particularly in decision making. In this paper, we propose a novel feature prediction method for temporal social networks, which estimates network measurements in the future based on a small window of measurements in the past. We utilized...
This paper presents a neural-network-based approach for the detection of misplaced and missing regions in images. The main objective of this project is to develop an intelligent system that can identify a misplaced or missing region of a tested image. The system can be used to detect misplaced and missing components of printed circuit boards during the manufacturing process. Jigsaw puzzle pieces can...
How to develop an intelligent ventilator and control it well to provide a better experience and treatment effect for respiratory patients is still a difficult task needed to be solved. The existing problems focus on the control algorithm and the mechanical structure. Dedicated to these two problems, the paper proposes a design of CPAP ventilator based on the ANN algorithm. Firstly, the paper introduces...
Data diversity in terms of types, styles, as well as radiometric, exposure and texture conditions widely exists in training and test data of vision applications. However, learning in traditional neural networks (NNs) only tries to find a model with fixed parameters that optimize the average behavior over all inputs, without using data-specific properties. In this paper, we develop a meta-level NN...
This article explores the problems of automated retail systems, which named are vending machines. The main problem is the formation of an assortment of a vending machine, the realization of which will bring maximum profit. As a modern analysis tool of consumer demand in retail trade artificial intelligence is regarded. Attention is focused on one of the methods of constructing artificial intelligence...
This paper considers architecture and functionality of the embedded data acquisition system for automated beehive monitoring. A description of constructed sensor subsystems is given. Proposed solution acquires hive temperature, humidity and weight referring this data to the mobile application via wireless network. The system also performs an analysis of collected bee noises with artificial neural...
One of many important activities in the Wireless Sensor Network is the localization for tracked devices. Received Signal Strength (RSS) is a parameter of the power level that being received by the radio which can be used to track the location of the devices. This paper evaluates the localization of ZigBee devices which uses RSS fingerprinting by artificial neural networks. The RSS data processing...
Training of Artificial Neural Networks (ANN) is an important step to make the network able to accomplish the desired task. This capacity of learning in such networks makes them applied in many applications as modeling and control. However, many of training algorithms have some drawbacks like: too many parameters to be estimated, important calculus time. In this paper, we propose a very simple method...
In a power distribution network, network topology information is essential for an efficient operation of the network. This information is not accurately available, due to uninformed changes that happen from time to time, or uncertain meter readings. Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly...
This paper proposes a neural network (NN) approach for demodulating output signals of a nonlinear channel with memory. The feed-forward neural network is trained to learn the appropriate mapping between nonlinear input patterns and source bits. The simulation results provide some evidence that neural networks can learn the effect of nonlinear channels with memory and demodulate the output signal of...
Artificial intelligence is widely used in image processing. Neural networks (NN) were successful used for solving complicated issues due to their capacity of generalization and learning from examples. In this paper some aspects of image compression using artificial neural networks are discussed. The network is used in the feedback loop of the visual servoing system, which aims to control a wheeled...
Smartphone ecosystems are considered as a unique source due to the large number of apps which in turn makes an extensive use of personal data. Currently, there is no privacy and security preservation mechanism in smartphone ecosystems to enable users to compare apps in terms of privacy and security protection level, and to alarm them regarding the invasive issues (in terms of privacy and security)...
Anemia is a condition in which the hemoglobin (Hb) content becomes less than that of the normal value. In this project, hemoglobin value is estimated using ANN (Artificial Neural Network). Database of blood sample images and their actual Hb values is collected from a local laboratory. Red, green and blue normalized values of images' samples are fed to the ANN as input. Cyanemethemoglobin method based...
In this paper, we implement hybrid Woodward-Lawson Neural Networks and weighted Fourier method to synthesize antenna arrays. The neural networks (NN) is applied here to simplify the modeling of MIMO antenna arrays by assessing phases. The main problem is obviously to find optimal weights of the linear antenna array elements giving radiation pattern with minimum sidelobe level (SLL) and hence ameliorating...
Automated Planning focuses on plan search. Traditionally, it aimed at domain-independent methods with handcrafted domain models. However, automated domain model acquisition, especially the action model acquisition is difficult. On the other hand, many problem specific search space pruning techniques were proposed. Therefore, we combine the automated domain model acquisition and problem specific search...
In the actual production process, the prediction of compressive strength of concrete 28d is of great significance. Prediction of compressive strength of concrete is a typical multi input single output nonlinear systems, which is very close to the BP neural network model. In this paper, the BP neural network is applied to the prediction of the compressive strength of concrete, but the training effect...
The dynamic and system reliability of driving system in battery electric vehicles (BEVs) highly depend on the fault diagnosis technology. In this paper, we provided a new data compression approach and validated it on a method based on neural network (NN) to detect both failures' types and degree in drive system. In time-/frequency domain several statistical features were extracted from signals acquired...
In this paper, by learning the origin of the word distributed representation, knowing the distributed representation is one of the bridges of natural language processing mapping to mathematical calculations. Through the learning distributed representation model: neural network language model, CBOW model and Skip-gram model, the advantages and disadvantages of each model are clarified. Through the...
This paper discusses how to apply the ensemble learning for the individual learners on the randomly splitting data. Rather than letting the individual learners learn independently on the different subsets, it would be better for the individual learners to learn cooperatively by exchanging the learned values. In this way, the individual learners could learn the whole given data together while they...
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