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نتیجه جستجو - Mobile device

تعداد مقالات یافته شده: 101
ردیف عنوان نوع
1 AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کم‌هزینه و سبز پسند برای پشتیبانی از تشخیص بیماری‌های برگ در زمان واقعی-2022
Agriculture is one of the most significant global economic activities responsible for feeding the world population of 7.75 billion. However, weather conditions and diseases impact production efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus, computational methods can support disease classification based on an image. This classification requires training Artificial Intelligence (AI) models on high-performance computing resources, usually far from the user domain. State of the art has proposed the concept of Edge Computing (EC), which aims to bring computational resources closer to the domain problem to decrease application latency and improve computational power closer to the client. In addition, EC has become an enabling technology for Smart Farms, and the literature has appropriated EC to support these applications. However, predominantly state-of-the-art architectures are dependent on Internet connectivity and do not allow diverse real-time classification of diseases based on crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with low-cost and green-friendly devices to support a mobile Smart Farm application, operational even in areas lacking Internet connectivity. Among our main contributions, we highlight the functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf images, achieving high classification performance using a smartphone. Our results indicate that AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing computational overhead on edge-compute. The AgroLens architecture opens up opportunities and research avenues for deployment and evaluation for large-scale Smart Farm applications with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly
مقاله انگلیسی
2 Secure mobile internet voting system using biometric authentication and wavelet based AES
سیستم رای گیری اینترنتی تلفن همراه با استفاده از احراز هویت بیومتریک و AES مبتنی بر موجک-2021
The number of mobile phone users increases daily, and mobile devices are used for various applications like banking, e-commerce, social media, internet voting, e-mails, etc. This paper presents a secure mobile internet voting system in which a biometric method authenticates the voter. The biometric image can either be encrypted at the mobile device and send to the server or process the biometric image at the mobile device to generate the biometric template and send it to the server. The implementation of biometrics on mobile devices usually requires simplifying the algorithm to adapt to the relatively small CPU processing power and battery charge. This paper proposes a wavelet-based AES algorithm to speed up the encryption process and reduce the mobile device’s CPU utilization. The experimental analysis of three methods(AES encryption, wavelet-based AES encryption, and biometric template generation) exhibits that wavelet-based AES encryption is much better than AES encryption and template generation. The security analysis of three methods shows that AES and wavelet-based AES encryption provides better security than the biometric template’s protection. The study of the proposed internet voting system shows that biometric authentication defeats almost all the mobile-based threats.
Keywords: Internet voting | Fingerprint template | Iris code | AES encryption | Wavelet based AES encryption
مقاله انگلیسی
3 GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors
GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی-2021
The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different lo- cations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and soft max for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%–1.96% for leave-one-out authentication.
Keywords: Biometric authentication | Gait authentication | Autoencoders | Sensor fusion | Multimodal learning | Wearable sensors
مقاله انگلیسی
4 Multifactor authentication scheme using physically unclonable functions
طرح احراز هویت چند عاملی با استفاده از توابع غیرقابل تنظیم فیزیکی-2021
We propose a secure telehealth system using multifactor authentication for the mobile de- vices as well as the IoT edge devices in the system. These two types of devices constitute the weakest link in telehealth systems. The mobile devices and edge devices are typically unsecured and contain vulnerable processors. The mobile devices use the healthcare professional’s biometric and endowing the edge device with biometrics is accomplished by using physically unclonable functions (PUFs). The embedded PUF acts as a means of enabling mutual authentication and key exchange. Evaluating the security of the proposed authentication scheme is conducted using three approaches: (a) formal analysis based on Burrows–Abadi–Needham logic (BAN); (b) informal security analysis for protection against many attack types.; (c) model checking using automated validation of internet security protocols and applications (AVISPA) tool.© 2020 Elsevier B.V. All rights reserved.1.
Keywords: Internet of Things | Device authentication | Hardware security | Embedded systems | PUF | AVISPA | BAN | Three-factor authentication
مقاله انگلیسی
5 Optokinetic response for mobile device biometric liveness assessment
پاسخ اپتوکینتیک برای ارزیابی زنده بودن بیومتریک دستگاه تلفن همراه-2021
As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an in- creasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, liveness based attack detection methods provide a po- tential deterrent. We present a novel optokinetc nystagmus (OKN) based liveness assessment system for mobile applications which leverages phase-locked temporal features of a unique reflexive behavioral response. In this paper we provide proof of concept for eliciting, collecting and extracting the OKN response motion signature on a mobile device. Results of our most successful experimental machine learning classifier are reported for a multi-layer LSTM based model demonstrating a 98.4% single stimulus detection performance for simulated video based attacks.© 2021 Elsevier B.V. All rights reserved.
Keywords: Biometrics | Eye movement | Ocular biometrics | Ocular kinetics | Digital identity | Mobile device security | Liveness | Behavioral biometrics
مقاله انگلیسی
6 Face recognition: fighting the fakes
Face recognition: fighting the fakes-2021
Facial biometrics have become increasingly popular authentication tools for three main reasons. First, the user experience is familiar. Onboarding is easy – basically like taking a selfie – and since cameras are already on mobile devices, they’re ubiquitously accessible. Second, they remove much, if not all of the friction that users encounter trying to remember passwords. Third, the technology works – and it’s getting better every year.
مقاله انگلیسی
7 Touch-based continuous mobile device authentication: State-of-the-art, challenges and opportunities
احراز هویت پیوسته دستگاه تلفن همراه مبتنی بر لمس: آخرین فن آوری ، چالش ها و فرصت ها-2021
The advancement in the computational capability and storage size of a modern mobile device has evolved it into a multi-purpose smart device for individual and business needs. The increasing usage of this device has led to the need for a secure and efficient authentication mechanism. For securing mobile devices, password, PIN, and swipe patterns are commonly used for user authentication. Entry-point face and fingerprint recognition have also gained traction in the past years. However, these authentication schemes cannot authenticate a user after the initial-login session. This limitation might put the device exposed to information theft and leakage if an illegitimate user could bypass the initial-login session. Therefore, a mobile device needs a continuous authentication mechanism that can protect a user throughout the entire working session, which complements the initial-login authentication to provide more comprehensive security protection. Touch biometric is a behavioural biometric that represents the touch behaviour pattern of a user when interacting with the touchscreen of the device. Touch biometric has been proposed as a continuous authentication mechanism, where the device can collect touch biometric data transparently while a user is using the device. However, there are still plenty of challenges and obstacles in touch-based continuous mobile device authentication due to its challenges as a biometric modality. This paper provides a comprehensive overview of fundamental principles that underpin touch-based continuous mobile device authentication. Our work discusses state-of- the-art methods in touch data acquisition, behavioural feature extraction, user classification, and evaluation methods. This paper also discusses some challenges and opportunities in the current touch-based continuous mobile device authentication domain to obtain a broad research community and market acceptance.
Keywords: Biometrics | Mobile device security | Continuous authentication | Touch biometric
مقاله انگلیسی
8 Behavioral biometrics & continuous user authentication on mobile devices: A survey
بیومتریک رفتاری و احراز هویت مداوم کاربر در دستگاه های تلفن همراه: یک مرور-2021
This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices. Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work. In our survey, we first present a classification of behavioral biometrics technologies and continuous authentication for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques. Then, we provide a state-of-the-art literature review focusing on the machine learning models performance in seven types of behavioral biometrics for continuous authentication. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. Finally, our discussions extend to lessons learned, current challenges and future trends.
Keywords: Machine Learning | Behavioral Biometrics | Continuous Authentication | Mobile Devices | Attacks | Defense | Survey
مقاله انگلیسی
9 Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020
With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity communication is regarded as a potential solution. It can use unlicensed bands to forward content to users under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition, deep learning-based methods almost need a lot of training time. Based on real-time requirements of the network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing (MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation.
Keywords: Opportunistic networks | MEC | Offloading | Content caching
مقاله انگلیسی
10 Reinforcement learning application in diabetes blood glucose control : A systematic review
کاربرد یادگیری تقویتی در کنترل قند خون دیابت : یک بررسی سیستماتیک-2020
Background: Reinforcement learning (RL) is a computational approach to understanding and automating goaldirected learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms.
Keywords: Reinforcement learning | Blood glucose control | Artificial pancreas | Closed-loop | Insulin infusion
مقاله انگلیسی
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