scholarly journals Localization using Neural Networks and Push-Pull Estimation based on RSS from AP to Mobile Device

2012 ◽  
Vol 19D (3) ◽  
pp. 237-246
Author(s):  
Seong-Jin Cho ◽  
Sung-Young Lee
Keyword(s):  
Neural Networks ◽  
Mobile Device

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR DETECTION OF MELANOMA USING SKIN LESION IMAGE ON MOBILE DEVICE

2021 ◽  
Vol 3 (1) ◽  
pp. 8-14
Author(s):  
D. V. Fedasyuk ◽  
◽  
T. V. Demianets ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Mobile Device ◽  
Mobile Application ◽  
High Accuracy ◽  
File Size ◽  
Melanoma Detection ◽  
The Neural Network ◽  
Classification Time

A melanoma is the deadliest skin cancer, so early diagnosis can provide a positive prognosis for treatment. Modern methods for early detecting melanoma on the image of the tumor are considered, and their advantages and disadvantages are analyzed. The article demonstrates a prototype of a mobile application for the detection of melanoma on the image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.

scholarly journals A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks

2021 ◽  
Vol 45 (2) ◽  
pp. 277-285
Author(s):  
A.V. Astafiev ◽  
D.V. Titov ◽  
A.L. Zhiznyakov ◽  
A.A. Demidov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sensor Network ◽  
Mobile Device ◽  
Experimental Studies ◽  
Test Sample ◽  
Small Scale ◽  
Approximation Function ◽  
Artificial Neural

The paper considers the development of a method for positioning a mobile device using a sensor network of BLE-beacons, the approximation of RSSI values and artificial neural networks. The aim of the work is to develop a method for positioning small-scale industrial mechanization equipment for building unmanned systems for product movement tracking. The work is divided into four main parts: data synthesis, signal filtering, selection of BLE beacons, translation of the RSSI values into a distance, and multilateration. A simplified Kalman filter is proposed for filtering the input signal to suppress Gaussian noise. A description of two approaches to translating the RSSI value into a distance is given: an exponential approximation function with a coefficient of determination of 0.6994 and an artificial feedforward neural network. A comparison of the results of these approaches is carried out on several test samples: a training one, a test sample at a known distance (0–50 meters) and a test sample at an unknown distance (60–100 meters). The artificial neural network is shown to perform better in all experiments, except for the test sample at a known distance (0–50 meters), for which the r.m.s. error is higher by 0.02 m 2 than that for the approximation function, which can be neglected. An algorithm for positioning a mobile device based on the multilateration method is proposed. Experimental studies of the developed method have shown that the positioning error does not exceed 0.9 meters in a 5×5.5 m room under monitoring. The positioning accuracy of a mobile device using the proposed method in the experiment is 40.9 % higher. Experimental studies are also conducted in a 58.4×4.5 m room, showing more accurate results compared to similar studies.

scholarly journals Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 509 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Mobile Device ◽  
Daily Living ◽  
Accelerometer Data ◽  
Pattern Recognition Techniques ◽  
Multiple Sensors ◽  
Computational Resources

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

scholarly journals Low-cost Prevention of the Spread of Viral Diseases Using Neural Networks

2021 ◽  
Vol 129 ◽  
pp. 02015
Author(s):  
Katerina Prihodova
Keyword(s):  
Neural Networks ◽  
Body Temperature ◽  
Infectious Diseases ◽  
Mobile Device ◽  
Low Cost ◽  
Value Added ◽  
Negative Consequences ◽  
Thermal Camera ◽  
Loss Of Appetite ◽  
Elevated Body Temperature

Research background: Globalization has both positive and negative consequences. For more than a year, the whole world has been feeling very strongly about one of the negative consequences of globalization. And that is the rapid spread of infectious diseases. Within a few months of the first COVID-19 diseases, a pandemic occurred. The most common symptoms of this disease are fever, muscle aches, fatigue, loss of appetite and difficulty breathing. Therefore, it is essential to control body temperature reliably. If the process of temperature monitoring takes place in closed spaces, and simultaneously, the identification of a person is necessary, we propose a low-cost solution. This consists of using a mobile device in combination with a thermal camera for capturing people and subsequent evaluation using classification methods. Purpose of the article: The aim of this article is to create a model of a system for self-shooting. Follows recognition of elevated body temperature of persons and their identification to reduce the global impact of COVID-19 on the economy and society. Methods: A mobile device (tablet) combined with a thermal camera is used as a sensor. This is followed by face detection in both visible and thermal images. Methods of artificial intelligence (convolutional neural networks) are used for subsequent classifications of individual persons. Findings & Value added: The proposed model of self-sensing and subsequent identification of persons and their classification into groups (increased body temperature, normal temperature). In places where it is necessary to identify people, the system also detects elevated body temperature. This will help fight the spread of infectious diseases, which are characterized by fever.

Recurrent Neural Networks for Predicting Mobile Device State

2019 ◽  
pp. 1028-1043
Author(s):  
Juan Manuel Rodriguez ◽  
Alejandro Zunino ◽  
Antonela Tommasel ◽  
Cristian Mateos
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Video Games ◽  
Mobile Devices ◽  
Mobile Device ◽  
Recurrent Neural Networks ◽  
Ad Hoc ◽  
Modern Life ◽  
Battery Charge ◽  
Current State

Nowadays, mobile devices are ubiquitous in modern life as they allow users to perform virtually any task, from checking e-mails to playing video games. However, many of these operations are conditioned by the state of mobile devices. Therefore, knowing the current state of mobile devices and predicting their future states is a crucial issue in different domains, such as context-aware applications or ad-hoc networking. Several authors have proposed to use different machine learning methods for predicting some aspect of mobile devices' future states. This chapter aims at predicting mobile devices' battery charge, whether it is plugged to A/C, and screen and WiFi state. To fulfil this goal, the current state of a mobile device can be regarded as the consequence of the previous sequence of states, meaning that future states can be predicted by known previous ones. This chapter focuses on using recurrent neural networks for predicting future states.

Identification of utility images on a mobile device

2020 ◽  
Vol 2020 (8) ◽  
pp. 269-1-269-6
Author(s):  
Karthick Shankar ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Neural Networks ◽  
Low Power ◽  
Mobile Phones ◽  
Mobile Device ◽  
Power Devices ◽  
Trade Off ◽  
Photo Album ◽  
The Media ◽  
Grocery Products ◽  
Looking Back

Mobile phones are used ubiquitously to capture all kinds of images – food, travel, friends, family, receipts, documents, grocery products and many more. Often times when looking back on photos to relive memories, we want to see images that actually represent experiences and not quick convenience photos that were taken for note-keeping and not deleted. Thus, we need to have a solution that presents only the relevant pictures without showing images of receipts, grocery products etc. – termed in general as utility images. This is in the context of a photobook which compiles and shows relevant images from the photo album of a mobile device. Further, all this has to be done on a mobile device since all the media resides there – introducing the need for our system to work on low power devices. In this paper, we present a work that can distinguish between utility and non-utility images. We also present a dataset of utility images and non-utility images with images for each category mentioned. Furthermore, we present a comparison between accuracies of popular pre-trained neural networks and show the trade-off between size and accuracy.

Recurrent Neural Networks for Predicting Mobile Device State

2018 ◽  
pp. 6658-6670
Author(s):  
Juan Manuel Rodriguez ◽  
Alejandro Zunino ◽  
Antonela Tommasel ◽  
Cristian Mateos
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Video Games ◽  
Mobile Devices ◽  
Mobile Device ◽  
Recurrent Neural Networks ◽  
Ad Hoc ◽  
Modern Life ◽  
Battery Charge ◽  
Current State

Nowadays, mobile devices are ubiquitous in modern life as they allow users to perform virtually any task, from checking e-mails to playing video games. However, many of these operations are conditioned by the state of mobile devices. Therefore, knowing the current state of mobile devices and predicting their future states is a crucial issue in different domains, such as context-aware applications or ad-hoc networking. Several authors have proposed to use different machine learning methods for predicting some aspect of mobile devices' future states. This work aims at predicting mobile devices' battery charge, whether it is plugged to A/C, and screen and WiFi state. To fulfil this goal, the current state of a mobile device can be regarded as the consequence of the previous sequence of states, meaning that future states can be predicted by known previous ones. This work focuses on using Recurrent Neural Networks for predicting future states.

Sign in / Sign up

Export Citation Format

Share Document