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نتیجه جستجو - Low-cost

تعداد مقالات یافته شده: 81
ردیف عنوان نوع
1 Deployment-Ready Quantum Key Distribution Over a Classical Network Infrastructure in Padua
توزیع کلید کوانتومی آماده استقرار بر روی یک زیرساخت شبکه کلاسیک در پادوآ-2022
Current technological progress is driving Quantum Key Distribution towards a commercial and worldwide scale expansion. Its capability to deliver secure communication regardless of the computational power of the attackers will be a fundamental feature in the next generations of telecommunication networks. Nevertheless, demonstrations of QKD implementation in a real operating scenario and their coexistence with the classical telecom infrastructure are of fundamental importance for reliable exploitation. Here we present a Quantum Key Distribution application implemented over a classical fiber-based infrastructure. We exploit a 50 MHz source at 1550 nm, a single 13 km-long fiber cable for both the quantum and the classical channel, and a simplified receiver scheme with just one single-photon detector. In this way, we achieve an error rate of approximately 2% and a secret key rate of about 1.7 kbps, thus demonstrating the feasibility of low-cost and ready-to-use Quantum Key Distribution systems compatible with standard classical infrastructure.
Index Terms: Classical channel | cryptography | fiber, FPGA | padua | POGNAC | quantum communication | quantum key distribution | qubit4sync | telecommunication.
مقاله انگلیسی
2 IoT-based Prediction Models in the Environmental Context: A Systematic Literature Review
مدل‌های پیش‌بینی مبتنی بر اینترنت اشیا در زمینه محیطی: مروری بر ادبیات سیستماتیک-2022
Undoubtedly, during the last years climate change has alerted the research community of the natural environment sector. Furthermore, the advent of Internet of Things (IoT) paradigm has enhanced the research activity in the environmental field offering low-cost sensors. Moreover, artificial intelligence and more specifically, statistical and machine learning methodologies have proved their predictive power in many disciplines and various real-world problems. As a result of the aforementioned, many scientists of the environmental research field have performed prediction models exploiting the strength of IoT data. Hence, insightful information could be extracted from the review of these research works and for this reason, a Systematic Literature Review (SLR) is introduced in the present manuscript in order to summarize the recent studies of the field under specific rules and constraints. From the SLR, 54 primary studies have been extracted during 2017-2021. The analysis showed that many IoT-based prediction models have been applied the previous years in 10 different environmental issues, presenting in the majority of the primary studies promising results.
keywords: Natural Environment | Internet of Things | Prediction Models | Systematic Literature Review
مقاله انگلیسی
3 A computer vision-based method for bridge model updating using displacement influence lines
یک روش مبتنی بر بینایی کامپیوتری برای به‌روزرسانی مدل پل با استفاده از خطوط موثر جابجایی-2022
This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system.
The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the coauthors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads.
The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements.
The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring.
keywords: Computer vision | Displacement influence line | Vehicle weigh-in-motion | Structural identification | Finite element method model | Model updating | Modal analysis | Bridge systems
مقاله انگلیسی
4 یک اسکریپت Matlab برای آنالیز مورفومتریک رودخانه‌ها، کانال‌ها و دره‌های روی زمینی، زیرآبی و فرا زمینی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 29
ویژگی های مورفومتریک نقش مهمی در طبقه بندی و مدل سازی سیستم های رودخانه ای دارند. تمرکز تحقیقات گذشته بر شباهت‌ بین سیستم‌های زیرآبی و فرازمینی ناشناخته و سیستم‌های رودخانه‌ای رو زمینی است، اما اکنون مطالعات جزئی و دقیق دره و کانال زیرآبی در دریاچه‌ها، مخازن، اقیانوس‌ها و سیستم‌های فرازمینی افزایش یافته است. در این مطالعات، اغلب فقط چند ویژگی مورفومتریک (به عنوان مثال، شیب بستر، پهنای کرانه، شعاع خط مرکزی در نوک خم، عمق کرانه‌) در نظر گرفته می شد، که علت آن فقدان ابزاری کارآمد برای تعیین این ویژگی‌ها بود. در این راستا، یک اسکریپت Matlab ساده برای تعیین مهم‌ترین ویژگی‌های مورفومتریک رودخانه‌ها، کانال‌ها و دره‌های رو زمینی، زیرآبی و فرازمینی ارائه شد. تنها ورودی‌های مورد نیاز این اسکریپت ، خاکریز یا تاج‌های کناره خاکریز است که تعریف خط مرکزی را به‌عنوان مبنای سیستم مرجع خمیده خطی کانال محور امکان‌پذیر می‌ کند و به محاسبه ویژگی‌های پلان‌فرم (به عنوان مثال، عرض کامل، انحنای تدریجی متغیر، سینوسی) می پردازد. در صورتی که داده‌های رقومی ارتفاع بیومتری یا توپوگرافی وجود داشته باشد و قابل تبدیل به سیستم مرجع خمیده خطی کانال‌مرکز باشند، بنابراین امکان تعیین شیب بستر طولی و ویژگی‌های بیشتر مورفومتریک در سطح مقطع های عرضی (به عنوان مثال، عمق کرانه، سطح مقطع، و شیب های کناره ها یا سیلاب ها) فراهم می شود. این اسکریپت به عنوان مثال بر دره زیر آبی در دریاچه کنستانس اجرا شد. این اسکریپت ابزاری کارآمد برای آنالیز مقدار روزافزون مدل‌های ارتفاعی دیجیتال (DEMs) در رودخانه‌ها، کانال‌ها و دره‌های رو زمینی، زیرآبی و فرازمینی است. این اسکریپت به ویژه برای سیستم‌های زیر آبی که درک آن ها ضعیف است، مناسب بوده و به درک بزرگترین سیستم‌های دره و کانال کمک می‌کند.
کلمات کلیدی: رانندگی خودکار | محلی سازی سطح لاین | تشخیص لاین | GNSS | GPS | تطبیق نقشه
مقاله ترجمه شده
5 محلی‌سازی دقیق سطح خط مبتنی بر تطبیق نقشه با استفاده از دوربین و GPS کم‌هزینه
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 14
در سیستم های خودران یا وسایل نقلیه خودران(وسایل بدون راننده) (AVs) ، محلی سازی دقیق سطح خط، برای انجام مانورهای پیچیده رانندگی ضروری است. معمولاً روش‌های کلاسیک مبتنی بر GNSS از دقت کافی برای محلی‌سازی در سطح خط و پشتیبانی از مانورهای AV برخوردار نیستند. محلی سازی مبتنی بر LiDAR قابلیت ارائه محلی سازی دقیق را دارد. با این حال، یکی از مسائل مهمی که مانع تبدیل کاربرد گسترده این نوع راه حل می شود، قیمت LiDAR است. بنابراین، در این پژوهش راه‌حلی کم‌هزینه برای محلی‌سازی سطح خط و برای دستیابی به محلی‌سازی با دقت بالا در سطح خط با استفاده از سیستم مبتنی بر دید و GPS کم‌هزینه پیشنهاد شد. آزمایش‌ها در دنیای واقعی و زمان واقعی اثبات می کند که روش پیشنهادی در محلی‌سازی سطح خط دقت مطلوبی داشته و عملکرد بهتری نسبت به راه‌حل‌های مبتنی بر فقط GPS ، ارائه داده است.
کلیدواژه: رانندگی خودران | محلی سازی سطح خط | تشخیص خط | GNSS| GPS| تطبیق نقشه
مقاله ترجمه شده
6 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
7 An integrated solution of software and hardware for environmental monitoring
راه حل یکپارچه نرم افزاری و سخت افزاری برای نظارت محیطی-2022
With the expansion of the Internet of Things (IoT), several monitoring solutions are available in the market. However, most solutions use proprietary software, which is costly and do not provide online monitoring, hampering data access and hindering preventive actions. This article presents LimnoStation, a low-cost integrated hardware and software solution that employs IoT concepts with LoRaWan, whose main objective is to monitor environmental and oceanographic data from surface and submerged sensors, which can be accessed online and has low-power consumption. Long-distance transmission tests were performed analyzing battery consumption and readings taken by the LimnoStation sensors. The results show that the average error of sensor readings was 0.51%, with a battery life of more than 2900 days and costing about 100 times less compared to commercial sensors. The evaluation of the LimnoStation showed that it is viable not only for academic use, but also as a replacement for presenting lower cost, high reliability, greater integration, and more functionality than most solutions found on the market.
Keywords: IoT | LoRaWan | LoRa | Environmental monitoring
مقاله انگلیسی
8 An IoT-enabled intelligent automobile system for smart cities
یک سیستم خودروی هوشمند مجهز به اینترنت اشیا برای شهرهای هوشمند-2022
In our world of advancing technologies, automobiles are one industry where we can see improved ergonomics and feature progressions. Artificial Intelligence (AI) integrated with Internet of Things (IoT) is the future of most of the cutting-edge applications developed for automobile industry to enhance performance and safety. The objective of this research is to develop a new feature that can enhance the existing technology present in automo- biles at low-cost. We had previously developed a technology known as Smart Accident Precognition System (SAPS) which reduces the rate of accidents in automobile and also enhance the safety of the passengers. Current research advances this technique by inte- grating Google Assistant with the SAPS. The proposed system integrates several embedded devices in the automobiles that monitor various aspects such as speed, distance, safety measures like seatbelt, door locks, airbags, handbrakes etc. The real-time data is stored in the cloud and the vehicle can adapt to various situations from the previous data collected. Also, with the Google Assistant user can lock and unlock, start and stop, alert and do var- ious automated tasks such as low fuel remainder, insurance remainders etc. The proposed IoT enabled real-time vehicle system can detect accidents and adapt to change according to various conditions. Further, with RFID keyless entry authentication the vehicle is secure than ever before. This proposed system is much efficient to the existing systems and will have a great positive impact in the automobile industry and society. © 2020 Elsevier B.V. All rights reserved.
keywords: هوش مصنوعی | سیستم هوشمند خودرو | اینترنت اشیا | شهرهای هوشمند | سیستم هوشمند | Artificial intelligence | Intelligent automobile system | Internet of Things | Smart Cities | Smart System
مقاله انگلیسی
9 Fast heuristic method to detect people in frontal depth images
روش سریع ابتکاری برای تشخیص افراد در تصاویر عمق جلو-2021
This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in Realtime using a low-cost CPU platform with a highaccuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of99.77%.
Keywords: 3D People detection | Depth camera | Frontal Depth images | Feature extraction | Head biometric classification
مقاله انگلیسی
10 Deep learning-based single image face depth data enhancement
افزایش عمق داده ها با یادگیری عمیق مبتنی بر تک تصویر-2021
Face recognition can benefit from the utilization of depth data captured using low-cost cameras, in particular for presentation attack detection purposes. Depth video output from these capture devices can however contain defects such as holes or general depth inaccuracies. This work proposes a deep learning face depth enhancement method in this context of facial biometrics, which adds a security aspect to the topic. U-Net-like architectures are utilized, and the networks are compared against hand-crafted enhancer types, as well as a similar depth enhancer network from related work trained for an adjacent application scenario. All tested enhancer types exclusively use depth data as input, which differs from methods that enhance depth based on additional input data such as visible light color images. Synthetic face depth ground truth images and degraded forms thereof are created with help of PRNet, to train multiple deep learning enhancer models with different network sizes and training configurations. Evaluations are carried out on the synthetic data, on Kinect v1 images from the KinectFaceDB, and on in-house RealSense D435 images. These evaluations include an assessment of the falsification for occluded face depth input, which is relevant to biometric security. The proposed deep learning enhancers yield noticeably better results than the tested preexisting enhancers, without overly falsifying depth data when non-face input is provided, and are shown to reduce the error of a simple landmark-based PAD method.
Keywords: 3D face depth | Deep learning | Image enhancement | Face depth synthesis | Face recognition | Presentation attack detection
مقاله انگلیسی
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