Low-end Smartphones Research Articles

Automated Surface Classification System Using Vibration Patterns—A Case Study With Wheelchairs

Finding barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility is yet to be investigated. In order to address this problem, we present the WheelShare system which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowd-sourcing. Based on this surface-specific vibration we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this paper include the following. (1) We have developed a machine learning enabled surface recognition system which uses sensor data to capture surface-induced vibration patterns. (2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. (3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing, and successfully recognized the surface characteristics.

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.

Edge-Compatible Deep Learning Models for Detection of Pest Outbreaks in Viticulture

The direct effect of global warming on viticulture is already apparent, with unexpected pests and diseases as one of the most concerning consequences. Deploying sticky traps on grape plantations to attract key insects has been the backbone of conventional pest management programs. However, they are time-consuming processes for winegrowers, conducted through visual inspection via the manual identification and counting of key insects. Additionally, winegrowers usually lack taxonomy expertise for accurate species identification. This paper explores the usage of deep learning on the edge to identify and quantify pest counts automatically. Different mobile devices were used to acquire a dataset of yellow sticky and delta traps, consisting of 168 images with 8966 key insects manually annotated by experienced taxonomy specialists. Five different deep learning models suitable to run locally on mobile devices were selected, trained, and benchmarked to detect five different insect species. Model-centric, data-centric, and deployment-centric strategies were explored to improve and fine-tune the considered models, where they were tested on low-end and high-end mobile devices. The SSD ResNet50 model proved to be the most suitable architecture for deployment on edge devices, with accuracies per class ranging from 82% to 99%, the F1 score ranging from 58% to 84%, and inference speeds per trap image of 19.4 s and 62.7 s for high-end and low-end smartphones, respectively. These results demonstrate the potential of the approach proposed to be integrated into a mobile-based solution for vineyard pest monitoring by providing automated detection and the counting of key vector insects to winegrowers and taxonomy specialists.

Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments.

Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images.

Α PATTERN-BASED AUGMENTED REALITY APPLICATION FOR THE DISSEMINATION OF CULTURAL HERITAGE

Abstract. Augmented Reality (AR) is more than an added value for Cultural Heritage (CH); it is vital for its sustainability, promotion and dissemination, increasing accessibility in CH even during difficult periods of time, like the Covid-19 pandemic. In order to be meaningful and engaging, an AR application should have the following characteristics: easiness of use, high-quality representations and compatibility. This paper presents a marker-less mobile AR application for the display and inspection of high-resolution 3D cultural assets, overlayed on a particular location in the real-world scene. Instead of predefined markers, an image captured by the user is exploited as a pattern for real-time feature matching, pose estimation and scene augmentation. Our approach is based on pure computer vision and photogrammetric techniques, implemented using native C++ and Java code for Android mobile platforms. It is built with the use of the OpenCV library and the OpenGL ES graphics API without any dependencies of AR Software Development Kits (SDKs). Therefore, it supports cross-vendor portability regarding mobile model devices and hardware specifications. The evaluation of the developed application examines the performance of various matching techniques and the overall responsiveness of processing and 3D rendering on mid-range and low-end smartphones. The results showcase the reliability and responsiveness of the pattern recognition as well as the potential of the 3D graphics engine to render and overlay complex 3D models balancing between visual quality and time. The proposed methodology is applied to the Ciborium of the church of St. Charalabos, located at St. Stephen’s Monastery in Meteora, Greece.

Sistem Pendukung Keputusan Pemilihan Smartphone Kelas Bawah Terbaik pada Toko Bram Cell dengan Metode Simple Additive Weighting (SAW)

Bram Cell store is a store that sells various types of low-end smartphones at affordable prices. However, the problem faced by the Bram Cell Store is processing data in selecting the best low-end smartphone, because there is no special system for selecting the best low-end smartphone, so it takes a long time to determine. To overcome this problem, this research was conducted with the aim of building a Decision Support System (DSS) by applying the Simple Additive Weighting (SAW) method which is useful for simplifying the smartphone selection process. The design of a decision support system using the SAW method produces applications that perform effective and efficient selection. The impact of this research is helping Bram Cell in selecting the best low-end smartphone and minimizing selection errors used in the previous systemKeywords: Decision Support System, Weight Product, Fabric

A Task Execution Scheme for Dew Computing with State-of-the-Art Smartphones

The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.

Determining Factors on the Repurchase Decision on Low End Smartphones in Asia Pacific Regions: An Indonesian Case

Nowadays, consumers are more aware regarding their decision to purchase a product, either for short-term or long-term use. There are several factors that lead the desire to repurchase in consumer perspective. This study tried to investigate the factors of price, experiential value, brand name, e-WOM, perceived value and perceived brand image as some variables deemed to have significant influences on the consumer repurchase behavior. This analysis uses quantitative analysis through AMOS analysis within 310 datasets to execute the hypothesis. The results of this research are defined that the price discount did not affect both on perceived brand image and value. The hypotheses testing showed that experiential value, brand name, e-WOM, perceived value and perceived brand image have significant effects on the consumer repurchase behavior. The practical implications posed that the growth of micro-financial companies which provided low down payment and long instalment without any discount offered are well accepted by consumer. The results of this study can be used as a direction for further study.
 
 Received: 4 March 2021 / Accepted: 6 May 2021 / Published: 8 July 2021

A New DGNSS Positioning Infrastructure for Android Smartphones.

One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

A Robust and Efficient Video Anti-Shaking Algorithm for Low-End Smartphone Platforms

Feature phones or low-end smart phones are an important part of our daily life. While lacking the powerful embedded camera and processing capability of the high-end smart phone, the feature phones with the low-end camera and processing capability are still widely used owing to its lower cost. Consequently, image and video processing algorithms are needed for many intelligent applications on the low-end smart phones. In this paper, we propose a novel robust and efficient video anti-shaking algorithm for the low-end smartphone platform. It provides an integrated application for the low-end smartphone platform, which includes a novel global motion estimation method, and a rapid and efficient local motion detection method. The overall framework is proposed considering the limited camera perception quality and limited mobile processing capability of the feature phone. The proposed algorithm can not only obtain a good visual effect but also be implemented in real-time for the low-end mobile platforms with very limited computational resource. Experimental results demonstrate that the proposed method can achieve a similar video stabilization effect to the Optical Image Stabilization hardware in high-end smartphones.

Efficient k-NN Implementation for Real-Time Detection of Cough Events in Smartphones.

The potential of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measurements of symptoms such as cough. Currently available cough detectors are uncomfortable and expensive at a time when generic smartphones can perform this task. However, two major challenges preclude smartphone-based cough detectors from effective deployment namely, the need to deal with noisy environments and computational cost. This paper focuses on the latter, since complex machine learning algorithms are too slow for real-time use and kill the battery in a few hours unless specific actions are taken. In this paper, we present a robust and efficient implementation of a smartphone-based cough detector. The audio signal acquired from the device's microphone is processed by computing local Hu moments as a robust feature set in the presence of background noise. We previously demonstrated that pairing Hu moments and a standard k-NN classifier achieved accurate cough detection at the expense of computation time. To speed-up k-NN search, many tree structures have been proposed. Our cough detector uses an improved vantage point (vp)-tree with optimized construction methods and a distance function that results in faster searches. We achieve 18× speed-up over classic vp-trees, and 560× over standard implementations of k-NN in state-of-the-art machine learning libraries, with classification accuracies over 93%, enabling real-time performance on low-end smartphones.

CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users

Recent advances in smartphones and location-aware services necessitate identifying logical locations of users, in terms of their surroundings, instead of raw location coordinates. In this paper, we have proposed CROWD-PAN-360 ( CP360 ), a novel smartphone-based system to generate 360-degree panoramic map of a querying user for his unfamiliar surrounding using crowd-sourced images. The objects (logical locations) appearing in the images are identified using manually or automatically generated tags. The system is context-aware and it intelligently associates user location coordinates with several smartphone contexts, like acceleration and orientation. CP360 can significantly reduce GPS positional errors for even cheap low-end smartphones and can identify the user surroundings very efficiently. We extensively tested the system in both indoor and outdoor environments of IIT Roorkee campus using Android smartphones over a dataset of more than 6,000 crowd-sourced images of nearly 70 objects (departments, hostels, cafeteria, etc.) and CP360 generates the panoramic map with an average accuracy of 92.2 percent.

Samsung to add biometric security to low-end smartphones

Samsung to add biometric security to low-end smartphones

모바일을 통한 아프리카 경제발전 전략 및 사례 연구

This study proposes strategies for economic development through mobile in Africa where mobile emerges as an infra to solve socioeconomic problems. It analyzes economy and ICT industry in Africa and studies several cases that are utilized in agriculture, finance, education, and healthcare. It also examines economic development theory and the impact of broadband on economic growth and development. It is necessary to leapfrog to mobile to achieve an information society and a compact industrialization based on ICT because fixed-line is very poor and expensive in Africa. Building a mobile broadband infra, supporting a dissemination of low-end smartphones and a government's support for mobile contents are necessary to achieve a leapfrogging development through mobile in Africa. The findings of this study provide additional insights for Korean ICT companies to consider a business in Africa.

A better sense of place

On the mobile phone as a `platform', location is everywhere. It may not be king, but pretty much any app that aims to provide a service now includes a location-based element, whether it is to help you find a nearby restaurant or minicab, or simply to push local promotions your way. The space-satellite-based Global Positioning System (GPS) is the key enabler. Even low-end smartphones now include not just a GPS chip, but can also locate themselves by scanning for local mobile basestations and Wi-Fi access points. Location-based services on mobile devices used to be the preserve of app-happy shoppers and geo-social networking. Now, their usefulness is being explored for more serious - even mission critical situations.