Android Mobile Devices Research Articles

EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone.

Objective Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.

웹과 안드로이드 모바일 연동 도서 재고 관리 시스템 설계 및 구현

웹과 안드로이드 모바일 연동 도서 재고 관리 시스템 설계 및 구현

Towards Parallelism Extraction for Heterogeneous Multicore Android Devices

Modern Android mobile devices are enabled by complex heterogeneous MPSoC platforms. To exploit the full potential of these hardware platforms, computationally intensive parts of applications have to be properly parallelized. However, the current practice involves several manual steps, which is a cumbersome task for programmers. In this paper, we present an automated approach to extract multiple forms of parallelism from native C code within Android applications, targeting heterogeneous multicore devices. We show the effectiveness of our approach by parallelizing a set of benchmarks on a Nexus 7 tablet, which is based on a Snapdragon MPSoC that features a quad-core Krait CPU cluster and an Adreno 320 GPU.

Mosquito (Diptera: Culicidae) Habitat Surveillance by Android Mobile Devices in Guangzhou, China.

In 2014, Guangzhou City, South China, suffered from its worst outbreak of dengue fever in decades. Larval mosquito habitat surveillance was carried out by using android mobile devices in four study sites in May 2015. The habitats with larval mosquitoes were recorded as photo waypoints in OruxMaps or in videos. The total number of potential mosquito habitats was 342, of which 166 (49%) were found to have mosquito larvae or pupae. Small containers were the most abundant potential habitats, accounting for 26% of the total number. More mosquito larvae and pupae, were found in small containers than in other objects holding water, for example, potted or hydroponic plants (p < 0.05). Mosquito larvae were collected from all plastic road barriers, used tires, and underground water. Aedes albopictus larvae were found from small and large containers, stumps, among others. The overall route index (RI) was 11.3, which was 14.2 times higher than the grade C criteria of the National Patriotic Health Campaign Committee (NPHCC), China. The higher RIs were found from the bird and flower markets, schools, and underground parking lots. The results indicated that Android mobile devices are a convenient and useful tool for surveillance of mosquito habitats, and the enhancement of source reduction may benefit the prevention and control of dengue vector mosquitoes.

A Comparative Demonstration and Analysis of File Sharing Applications on Android Mobile Devices

A Comparative Demonstration and Analysis of File Sharing Applications on Android Mobile Devices

EdTAM: Efficient Detection of Theft Android Mobile

Objectives: This paper describes to improve the android mobile device theft tracking technique Methods/Statistical Analysis: The proposed framework uses SMS in offline, MMS in online and Camera (Front) to capture the picture. The user has to provide the alternative mobile number and email ID after installing the application. The application start working on its background and it check for the IMSI (SIM card) number, if the SIM changes and take the snapshot by using Camera and send the SMS to the alternative number without his/her knowledge. Findings: The SMS contains the new SIM number and we can track the mobile. GPS system is used to identify the location. Application/ Improvement: The existing technique that runs always in background which leads memory leak, but here the system will start whenever mobile instrument restart or change the SIM card and start works.

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG.

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10–20 EEG electrodes with Yeo’s seven functional connectivity networks.

Anubhav: recognizing emotions through facial expression

We present a computer vision-based system named Anubhav (a Hindi word meaning feeling) which recognizes emotional facial expressions from streaming face videos. Our system runs at a speed of 10 frames per second (fps) on a 3.2-GHz desktop and at 3 fps on an Android mobile device. Using entropy and correlation-based analysis, we show that some particular salient regions of face image carry major expression-related information compared with other face regions. We also show that spatially close features within a salient face region carry correlated information regarding expression. Therefore, only a few features from each salient face region are enough for expression representation. Extraction of only a few features considerably saves response time. Exploitation of expression information from spatial as well as temporal dimensions gives good recognition accuracy. We have done extensive experiments on two publicly available data sets and also on live video streams. The recognition accuracies on benchmark CK\(+\) data set and on live video stream by our system are at least 13 and 20 % better, respectively, compared to competing approaches.

Artemisa: A Personal Driving Assistant for Fuel Saving

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent.

APLIKASI ISC (INFORMATICS STUDENT CENTER) MENGGUNAKAN METODE PERSONAL EXTREME PROGRAMMING BERBASIS ANDROID

Gathering for informations today can be classified as a primary need. Various kinds of activities can be done using technology, especially smartphones, for example, learning, interacting with other people, searching for information, or just for entertainment. There are many kinds of activities and  many variety to access them. So it cannot be done all at once in a practical way, we need a solution to be able to carry out these activities at once with easy access. The above issue is one of the problems which is faced by the University of Indo Global Mandiri (UIGM). In order for learning and getting information can be done at once and easily accessible, then the researcher do research that aims to build applications Informatics Student Center  (ISC) which is developed by using the Personal Extreme Programming (PXP) consisting of several phases, such as requirements, planning, iteration initialization, design, implementation, and system testing. ISC applications using the Android operating system, built with the collaboration of the Java language, PHP, and MySQL database. In addition to ease of access, the ISC has some features that academic information, discussion forums, entertainment, and job opportunity. Based on test results, it was concluded that the ISC application can run on Android mobile devices which are tested. Result from this study is the availability of applications supporting learning activities and access information that can be accessed online through the Android mobile devices

System Call Analysis of Android Malware Families

Background/Objectives: Now a days, Android Malware is coded so wisely that it has become very difficult to detect them. The static analysis of malicious code is not enough for detection of malware as this malware hides its method call in encrypted form or it can install the method at runtime. The system call tracing is an effective dynamic analysis technique for detecting malware as it can analyze the malware at the run time. Moreover, this technique does not require the application code for malware detection. Thus, this can detect that android malware also which are difficult to detect with static analysis of code. As Android was launched in 2008, so there were fewer studies available regarding the behavior of Android Malware Families and their characteristics. The aim of this work is to explore the behavior of 10 popular Android Malware Families focused on System Call Pattern of these families. Methods/Statistical Analysis: For this purpose, the authors have extracted the system call trace of 345 malicious applications from 10 Android Malware Families named FakeInstaller, Opfake, Plankton, DroidKungFu, BaseBridge, Iconosys, Kmin, Adrd and Gappusin using strace android tool and compared it with the system calls pattern of 300 Benign Applications to justify the behavior of malicious application. Findings: During the experiment, it is observed that the malicious applications invoke some system calls more frequently than benign applications. Different Android malware invokes the different set of system calls with different frequency. Applications/Improvements: This analysis can prove helpful in designing intrusion-detection systems for an android mobile device with more accuracy.

Image descriptor extraction using Android mobile devices for a video search system

The increasing amount of multimedia data makes it challenging to search for content, specially over an unlabeled database. Meanwhile, the proliferation of smartphones, which possess both video cameras and powerful processors, makes it possible to use these devices as part of a video search process. This paper presents a system for performing video searches using an Android mobile device, that records a video and extract one of three different kinds of global image descriptors, then it sends them to a server to perform a similarity search over a database. The precision of each kind of descriptor was measured. The efficiency of the system was compared with an analogous implementation which sends entire videos to the server and it was found to improve the system's response time and data usage.

모바일 영어말하기평가 융합 시스템 모형 타당도 검사

본 연구에서는 모바일기반 영어말하기평가 융합 모형을 개발하였다. 대학생들에게 요구분석을 실시한 결과를 바탕으로 아이폰과 안드로이드 모바일기기용 평가 앱을 개발하고, 그 시험타당도 검사를 실시하였다. 영어말하기 평가 개발을 위하여 시험문항, 채점요인, 척도를 개발하였다. 요구분석에서 대학생 총 89명 중 69명(79.3%)은 안드로이드폰을, 17명(19.5%)은 아이폰을 사용하고 있었다. 이 결과를 기반으로 iOS와 안드로이드 OS용 영어말하기평가 앱을 개발하였다. 이를 위해 HTML5, Javascript bridge 기술, Node와 Express, Mongo DB 등의 기술을 적용하였다. 실험에서는 5명의 수험자를 대상으로 평가를 시행하여 모든 기능이 정상적으로 작동함을 확인하고 수험자들의 피드백을 수집함으로써 시험타당도를 확보하였다. In the study, a convergence model of a mobile-based English speaking test was developed. Based on the needs analysis from Korean college students, a speaking test App for iOS and Android mobile devices was developed and its test validity was examined. To develop the test, test items, scoring features and scales were developed. The result of the needs analysis survey showed that 69 out of 87 (79.3%) owned Android phones whereas only 17 (19.5%) held iPhones. Based on the result, an English speaking test App was developed. The technologies used for the test included HTML5, the Javasciript bridge technology, Node and Express, and Mongo DB. In the experiment, five examinees took the test. The test validity was obtained in that all the functions of the test were successfully operated and some feedback was gathered.

Tecnologías de posicionamiento en interiores al servicio de una biblioteca universitaria: hacia la smart library

This article is a case study of the application of geospatial technologies in an academic library. More specifically, two applications were developed to assist users in finding the location of books, one for Android mobile devices and the other for web browsers. These applications provide users with the ability to plot the path from the point where the user is located to the shelf where the items are found, thereby improving the user experience. Additionally, this article investigates the possibility of applying geographic information systems (GIS) and geospatial technologies to library services.

The Design of Smart sphygmomanometer based on Android Mobile Device

with a growing number of hypertensive patients blood pressure measurement needs, this paper designed the intelligent wearable device based on Android phones. Mainly introduces the design and implementation of hardware and software of the Android mobile phones and STM32 as the core, individual blood pressure and heart rate control and measurement. Implements of the Android WiFi control of blood pressure, blood pressure measurement and abnormal warning s a good way to meet the demand for the hypertension patients on blood pressure measuremen, has the characteristics of high performance, low cost and low power consumption.

BlueDetect: An iBeacon-Enabled Scheme for Accurate and Energy-Efficient Indoor-Outdoor Detection and Seamless Location-Based Service.

The location and contextual status (indoor or outdoor) is fundamental and critical information for upper-layer applications, such as activity recognition and location-based services (LBS) for individuals. In addition, optimizations of building management systems (BMS), such as the pre-cooling or heating process of the air-conditioning system according to the human traffic entering or exiting a building, can utilize the information, as well. The emerging mobile devices, which are equipped with various sensors, become a feasible and flexible platform to perform indoor-outdoor (IO) detection. However, power-hungry sensors, such as GPS and WiFi, should be used with caution due to the constrained battery storage on mobile device. We propose BlueDetect: an accurate, fast response and energy-efficient scheme for IO detection and seamless LBS running on the mobile device based on the emerging low-power iBeacon technology. By leveraging the on-broad Bluetooth module and our proposed algorithms, BlueDetect provides a precise IO detection service that can turn on/off on-board power-hungry sensors smartly and automatically, optimize their performances and reduce the power consumption of mobile devices simultaneously. Moreover, seamless positioning and navigation services can be realized by it, especially in a semi-outdoor environment, which cannot be achieved by GPS or an indoor positioning system (IPS) easily. We prototype BlueDetect on Android mobile devices and evaluate its performance comprehensively. The experimental results have validated the superiority of BlueDetect in terms of IO detection accuracy, localization accuracy and energy consumption.

A Practical Design and Implementation of a Low Cost Platform for Remote Monitoring of Lower Limb Health of Amputees in the Developing World

In many areas of the world accessing professional physicians “when needed/as needed” might not be always possible for a variety of reasons. Therefore, in such cases, a targeted e-Health solution to safeguard patient long-term health could be a meaningful approach. Today’s modern healthcare technologies, often built around electronic and computer-based equipment, require an access to a reliable electricity supply. Many healthcare technologies and products also presume access to the high speed internet is available, making them unsuitable for use in areas where there is no fixed-line internet connectivity, access is slow, unreliable, and expensive, yet where the most benefit to patients may be gained. In this paper, a full mobile sensor platform is presented, based around readily-purchased consumer components, to facilitate a low cost and efficient means of monitoring the health of patients with prosthetic lower limbs. This platform is designed such that it can also be operated in a standalone mode, i.e., in the absence of internet connectivity, thereby making it suitable to the developing world. Also, to counter the challenge of power supply issues in e-Health monitoring, a self-contained rechargeable solution to the platform is proposed and demonstrated. The platform works with an Android mobile device, in order to allow for the capture of data from a wireless sensor unit, and to give the clinician access to results from the sensors. The results from the analysis, carried out within the platform’s Raspberry Pi Zero, are demonstrated to be of use for remote monitoring. This is specifically targeted for monitoring the tissue health of lower limb amputees. The monitoring of residual limb temperature and gait can be a useful indicator of tissue viability in lower limb amputees especially those suffering from diabetes. We describe a route wherein non-invasive monitoring of tissue health is achievable using the Gaussian process technique. This knowledge will be useful in establishing biomarkers related to a possible deterioration in a patient’s health or for assessing the impact of clinical interventions.

WATI: Warning of Traffic Incidents for Fuel Saving

Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.

A threat monitoring system in enterprise networks with smart mobiles

With the development of modern mobile operating systems, computing, and communication technologies, smart mobile devices have been widely used to support rich applications and have been integrated into enterprise networks for organisations to improve business operations. When accessing sensitive pieces of personal and business information, the lack of strong security in smart mobile devices has become a serious issue. In this paper, we developed a threat monitoring system to monitor and detect threats in enterprise networks with mobile devices. We implemented both signature and anomaly based schemes to monitor and detect threats. To evaluate the effectiveness of our threat monitoring system, we used real-world samples of benign apps and malware samples to conduct experiments on Android mobile devices. Our experimental data shows that our developed system can accurately and effectively detect malware on the Android platform while incurring low overhead to the system in terms of energy and CPU usage.

Control and Teleoperation of Robot Khepera via Android Mobile Device through Bluetooth and WiFi

Abstract: The paper describes the design and implementation of applications for control mobile robot Khepera III via mobile device on the platform Android. Communication between the mobile device and the mobile robot was realized through Bluetooth and WiFi interface. The solution of robot control is capable of teleoperation, following line, avoiding obstacles, and face recognition. Application takes a photo with USB camera placed on the robot. Then, this photo is sent to a mobile device, which recognizes a face. This solution demonstrates the use of smart phone as a control unit without use of computer. Image recognition is running on smart phone with Android as the operation system.