Development frameworks for mobile devices

Digital video playback on mobile devices is fast becoming widespread and popular. Since mobile devices are typically resource constrained in terms of network bandwidth, battery power and available screen resolution, it is often necessary to formulate special encoding techniques in order to optimize power consumption during video streaming and playback. The existing H.264 standard is popular for video encoding on mobile devices, since it results in a low-bitrate video with visual clarity that is adequate for video playback on mobile devices. However, due to the complexity of the H.264 representation, the video decoding procedure is typically computationally intensive. In this paper, we propose a novel lossy video representation termed as Ligne-Claire (LC) video. LC videos are obtained via graphics overlay of outlines or silhouettes of objects in the video over an approximated texture video. Since the playback of LC video is typically meant for mobile devices, the visual quality of video is adequate for most mobile applications wherein the semantic content of the video can be characterized by object shapes and approximate texture information. Experimental results presented in the paper demonstrate that the proposed lossy LC video encoding scheme results in power savings of 50% or more during video playback compared to standard H.264-encoded videos, of similar video file size. In order to evaluate the visual quality of the LC video, we compare the performance of LC videos with H.264-encoded videos in the context of some typical computer vision tasks. Our results indicate that the performance of the computer vision algorithms on these videos is similar. This fact, coupled with subjective evaluation, and the resulting significant power savings, indicates that the proposed LC representation can be used effectively to encode video for power-constrained mobile devices.

Brain-computer interface speller system design from electroencephalogram signals with channel selection algorithms

This research examined how brand congruity in joint advertising affects individuals’ processing of advertising messages. An experiment was conducted whereby ads promoting two congruent or incongruent brands were presented to subjects under low versus high processing load. Results showed that advertising messages for congruent brands were better remembered under both high and low processing loads. However, ads with congruent brands led to positive attitudes only when subjects’ processing resources were constrained. These findings suggest congruent brands that appear in joint ads have a clear advantage over incongruent brands. The results have both practical and theoretical implications.

Working memory capacity in schizophrenia: a parametric fMRI study

Recent years have witnessed a significant rise in the number, duration and variety of video contents, which contribute to the bulk of internet traffic. With increase in smartphone and tablet users, watching videos on mobile devices has become one of its most popular use cases. These devices live on limited battery energy which is still a major bottleneck and a source of user dissatisfaction during video playback. In this paper we introduce an intermediate framework called VIDalizer for power efficient video delivery to smartphones and tablets. This almost transparent to the user, battery aware framework takes away some of the video processing overhead from the device and intelligently tunes its parameters customized for the mobile device while delivering the video using a novel transport protocol. Our preliminary results show that this framework can significantly reduce energy consumption up to 45%–55% of a mobile device without compromising user experience.

The vast amount of electronic health records (EHRs) is a norm to specify the health data in a digital world. As the amount of data starts to grow, handling the tasks on the basis of attributes such as historical performance, computational capacity, and workload results in a serious problem. The high processing loads can consume more resources and result in inefficiencies or delays in using and evaluating sensitive health data. With the emergence of the internet of things (IoT), artificial intelligence (AI), machine learning, and deep learning strategies, data‐driven applications become a promising solution for designing robust diagnostic approaches utilizing healthcare data. These mechanisms obtained much attention from industries and the educational sectors and resulted in better advancements in healthcare models. The AI‐driven approaches associated with healthcare applications still encounter some problems including security, privacy, and the quality‐of‐service (QoS). Machine learning with privacy‐preserving approaches safeguards the data; yet still, it is complex to formulate the infrastructural facilities. Certain problems are efficiently resolved by federated learning (FL) since it enables personal communication between distinct technologies and lightens the system's computational overload. Moreover, it efficiently manages the EHR data. Therefore, this work designs an efficient AI‐driven load distribution in the federated network by considering the limitations of the existing mechanisms. In the developed framework, at first, the essential EHRs are collected from the benchmark resources and provided to the FL network to resolve the overloading issues. Further, the EHR data is used to detect the disease using an Adaptive Temporal Convolution Network with Attention Mechanism (ATCN‐AM) in the FL network. Moreover, the parameters in the ATCN‐AM model are tuned using Improved Dung Beetle Optimizer (IDBO). Finally, different experimental validations are performed in the developed framework over the conventional mechanisms.

Motion estimation in videos is a computationally intensive process. A popular strategy for dealing with such a high processing load is to accelerate algorithms with dedicated hardware such as graphic processor units (GPU), field programmable gate arrays (FPGA), and digital signal processors (DSP). Previous approaches addressed the problem using accelerators together with a general purpose processor, such as acorn RISC machines (ARM). In this work, we present a co-processing architecture using FPGA and DSP. A portable platform for motion estimation based on sparse feature point detection and tracking is developed for real-time embedded systems and smart video sensors applications. A Harris corner detection IP core is designed with a customized fine grain pipeline on a Virtex-4 FPGA. The detected feature points are then tracked using the Lucas---Kanade algorithm in a DSP that acts as a co-processor for the FPGA. The hybrid system offers a throughput of 160 frames per second (fps) for VGA image resolution. We have also tested the benefits of our proposed solution (FPGA + DSP) in comparison with two other traditional architectures and co-processing strategies: hybrid ARM + DSP and DSP only. The proposed FPGA + DSP system offers a speedup of about 20 times and 3 times over ARM + DSP and DSP only configurations, respectively. A comparison of the Harris feature detection algorithm performance between different embedded processors (DSP, ARM, and FPGA) reveals that the DSP offers the best performance when scaling up from QVGA to VGA resolutions.

to present a challenge in terms of performance, robustness and accuracy. In particular mobile augmented reality applications are in need of tracking systems which can be wearable and do not cause a high processing load. In this paper we introduce a novel iterative geometric method for pose estimation from four co-planar points and we present the current status of PTrack, a marker-based single camera tracking system benefiting from this approach. The system uses an IDS uEye [1] camera, equipped with infrared flash strobes and infrared pass filter, which acquires grayscale images and sends them to a computer where an image pre-processing algorithm identifies potential projections of retro-reflective markers. Our novel pose estimation algorithm identifies possible labels composed of markers in a 2D post-processing using a divide-and-conquer strategy to segment the camera's image space and attempts an iterative geometric 3D reconstruction of position and orientation in camera space. In the end successfully reconstructed labels are compared to a database for identification. Their 6 DoF data as well as their ID is made available to applications through OpenTracker [2] framework. To assess the performance of our approach we compared PTrack to ARToolKit [3] - which has been adapted to work with the same camera - and final results show that pose estimation is more accurate and precise, in both translation and rotation.

This study presents a novel method for wireless charging of mobile devices, leveraging solar energy as a power source. The system integrates inductive power transfer technology to enhance efficiency by converting radio frequency signals into direct current effectively. This approach enables concurrent charging and energy harvesting, thereby maximizing power utilization. By incorporating solar energy, the system reduces dependence on conventional power outlets, promoting sustainability in device charging practices. This innovative solution represents a significant advancement in wireless charging technology, promising increased convenience and environmental friendliness for mobile device users. In summary, the proposed system offers a dual functionality of wireless charging and solar energy harvesting for mobile devices. By utilizing inductive power transfer technology, it optimizes the conversion of radio frequency signals to direct current, ensuring efficient energy utilization. This integration of solar energy not only facilitates sustainable charging but also reduces reliance on grid-based electricity, contributing to environmental preservation. Consequently, the proposed solution presents a compelling advancement in wireless charging technology, promoting convenience and eco-friendliness in powering mobile devices.

Recent mobile devices adopt high-performance processors to support various functions. As a side effect, higher performance inevitably leads to power density increase, eventually resulting in thermal problems. In order to alleviate the thermal problems, off-the-shelf mobile devices rely on dynamic voltage-frequency scaling (DVFS)-based dynamic thermal management (DTM) schemes. Unfortunately, in the DVFS-based DTM schemes, an excessive number of DTM operations worsen not only performance but also power efficiency. In this paper, we propose a temperature-aware DVFS scheme for Android-based mobile devices to optimize power or performance depending on the option. We evaluate our scheme in the off-the-shelf mobile device. Our evaluation results show that our scheme saves energy consumption by 12.7%, on average, when we use the power optimizing option. Our scheme also enhances the performance by 6.3%, on average, by using the performance optimizing scheme, still reducing the energy consumption by 6.7%.

Mobile technologies enable individuals to organize and manage their daily li ves through the mobile applications that are use d during the day. A ugmented reality is one of those applications that can be used on mobile devices. Expectations for the use of new technologies in educational environments increase in parallel with the technolog ical advancements. Thanks to advances in computer, internet and mobile technologies, numerous new applications have emerged that facilitate communication and interaction in educational environments. In particular, t he applications of the augmented reality technologies emerging with the advancing technology in the field of education are becoming widespread and easy -to-use. Augmented reality is a technology that enriches the learning process, created by real-time integration of the virtual elements and the real world elements. In geography teaching, different knowledge and skills are expected to be acquired by the learner , therefore augmented reality application s appeal ing to the different senses of learners can be utilized. AR technology can be used in various fields , especially in education. In addition to increasing productivity, mobile augmented reality applications would be used for multiple purposes in the field of education with the interactive facilities they offer to user such as 2D and 3D visual support and video playback.

The critical need for higher power efficiency and bandwidth transceiver design has significantly increased as mobile devices, such as smart phones, laptops, tablets, and ultra-portable personal digital assistants continue to be constructed using heterogeneous intellectual properties such as central processing units (CPUs), graphics processing units (GPUs), digital signal processors, dynamic random-access memories (DRAMs), sensors, and graphics/image processing units and to have enhanced graphic computing and video processing capabilities. However, the current mobile interface technologies which support CPU to memory communication (e.g. baseband-only signaling) have critical limitations, particularly super-linear energy consumption, limited bandwidth, and non-reconfigurable data access. As a consequence, there is a critical need to improve both energy efficiency and bandwidth for future mobile devices.;The primary goal of this study is to design an energy-efficient reconfigurable mobile memory interface for mobile computing systems in order to dramatically enhance the circuit and system bandwidth and power efficiency. The proposed energy efficient mobile memory interface which utilizes an advanced base-band (BB) signaling and a RF-band signaling is capable of simultaneous bi-directional communication and reconfigurable data access. It also increases power efficiency and bandwidth between mobile CPUs and memory subsystems on a single-ended shared transmission line. Moreover, due to multiple data communication on a single-ended shared transmission line, the number of transmission lines between mobile CPU and memories is considerably reduced, resulting in significant technological innovations, (e.g. more compact devices and low cost packaging to mobile communication interface) and establishing the principles and feasibility of technologies for future mobile system applications. The operation and performance of the proposed transceiver are analyzed and its circuit implementation is discussed in details. A chip prototype of the transceiver was implemented in a 65nm CMOS process technology. In the measurement, the transceiver exhibits higher aggregate data throughput and better energy efficiency compared to prior works.

The e-health domain has for objective to assist and manage citizens health. It concerns many actors like patient, doctors, hospitals and administration. Current and forthcoming generations of application will be web based and will integrate more and more mobile devices. In such application domain, dependability is a key notion. This paper presents, through a case study, how we can develop an application that controls the insulin injection and that is embedded in a mobile device belonging to an e-health Web Information System (WIS). In order to ensure the dependability of the control systems, we show how to use coordinated atomic actions (CAA). In order to implement our design, we explain how to use a development framework that we have made to implement CAA, which originally was not tailored for mobile fault-tolerant applications. Thus, in this paper, we also explain how we have adapted and used CAA-DRIP for mobile devices.

In this paper, a novel self-punishment based scheduling algorithm for a cooperative real- time content download application is designed. In the proposed protocol, selfish mobile devices autonomously form cooperative groups. For each formed group, the base station transmits the content to a selected mobile device designated as seed. Then, the seed shares the content with other mobile devices called sinks over device-to-device links. After analyzing the proposed protocol using a repeated game, new self-punishment mechanisms by revocation or by decreasing the bit rate, are proposed. Such self-punishment mechanisms enable the mobile devices in each cooperative group to autonomously punish selfish seeds without requiring any help from other mobile devices outside cooperative group. ‌Based on the proposed self-punishment mechanisms, a fair algorithm is designed to schedule the seeds in each cooperative group. Then, the designed scheduling algorithm is implemented using an Android application that is developed using Java in the Android Development Tool Bundle. The developed Android application does not depend on the operation system of mobile devices. Simulation results demonstrate that, on the average, the proposed protocol improves the energy efficiency of mobile devices to download real-time content of around 42 % compared to a traditional multicast scenario. Moreover, the proposed protocol does not let the energy efficiency of mobile devices degrade more than 11 %, on average, from the optimal solution even when all the mobile devises are selfish. The practical results show that the maximum difference in the run time of a real- time video over real-world smartphone screens is less than 500 milliseconds when the smartphones form a cooperative group using the developed Android application.

Today, an increasing amount of trades would be improving these new skills of internet and smart technology as the main tool to promote their products and services. Increasing the skill and ability both to attract the new consumers of young and elder in smart business context is an important challenge for trade managers and has described their considerable attention from the academic groups. More recently, driven by advance developments in Information and Communications Technologies (ICTs) and Mixed-Reality (MR) technologies, Virtual-Reality (VR), and Augmented-Reality (AR), embodied devices, sensors, mobile terminal device, microchip design, power efficiency and more broadly the Internet of Things (IoT), the chance to remotely connect to physical and virtual objects, mobile devices and products would have given rise to the appearance of smart technologies and smart services. Therefore, these businesses directors imminently want to realize the connections with consumer motivation, trust, security risk and the behavioral intention of consumer in requirement of user's utilizing smart technologies, in order to capture a crucial chance from now on smart trade proliferation to find those possible users, The primary concern in this article would be to understand the need and inspect these influences of sensing on perceived value, consumer's reputation, trust and to employ the smart technology of user behavioral intention. Next, the questionnaire would be made complete by online investigation proof ( $N=92$ ) collected from those active users who would have attended in using smart technologies. Thus, analyzed results with confirmatory factor analysis, and structural equation modeling would be also used, applying the SmartPLS 2.0, to certify if these questionnaire proofs would get proper to research model. Lastly, these outcomes would apply the evidence that perceived value, consumer's reputation and trust all could affect behavioral intention to employ smart technologies. All these consequences of the article with significances for practice and theory could be more cared enough to keep in mind, too.