The communication between the devices needs to be secured because it takes place over the internet. The Android device connects to the LabVIEW application, working as a remote interface to the wind turbine. The wind turbine workstation contains a LabVIEW program which monitors the entire wind turbine energy conversion system (WECS). This paper describes the remote control of a wind turbine system over the internet using an Android device, namely a tablet or a smartphone. Wind turbine remote control using Android devices According to the Saferoot website, the process of.is applicable for the Samsung Galaxy S3 as well as many other Android devices, but there are several steps involved in rooting an Android device (as Hat Enterprise Linux, version 6.5 • Android Development Tools (ADT), version 22.3.0-887826 • Saferoot1 • Samsung Galaxy S3 • Dell Precision thod used for the Samsung Galaxy S3 is called Saferoot1—a well- known, open- source software. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions. The review reveals the complete lack of a reference framework to validate and compare the proposals. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. PMID:26213928Īnalysis of Android Device-Based Solutions for Fall Detection.įalls are a major cause of health and psychological problems as well as hospitalization costs among older adults. We evaluate our proposed algorithm by using publicly available ORL database and facial images captured by an Android tablet.Īnalysis of Android Device-Based Solutions for Fall DetectionĬasilari, Eduardo Luque, Rafael Morón, MarÃa-Joséįalls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Verification results with different type of histogram based features are first obtained separately and then combined by weighted averaging. The proposed system verify the detected face using histogram based features, which are generated by binary Vector Quantization (VQ) histogram using DCT coefficients in low frequency domains, as well as Improved Local Binary Pattern (Improved LBP) histogram in spatial domain. In this system, facial image is captured by a built-in camera on the Android device firstly, and then face detection is implemented using Haar-like features and AdaBoost learning algorithm. This paper proposes a face verification system that runs on Android mobile devices. Face verification system for Android mobile devices using histogram based features
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