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نتیجه جستجو - تشخیص

تعداد مقالات یافته شده: 1173
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41 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
42 Single-lead ECG based multiscale neural network for obstructive sleep apnea detection
شبکه عصبی چند مقیاسی مبتنی بر ECG تک سرب برای تشخیص آپنه انسدادی خواب-2022
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing during sleep, which cannot be easily diagnosed at the early stage due to the complexity and labor intensity of the polysomnography (PSG). Using a ECG device for OSA detection provides a convenient solution in the current Internet of Things scenario. However, previous intelligent analysis algorithms mainly rely on single scale network, therefore the discriminative ECG representations cannot be identified, which affects the accuracy of OSA detection. We report a multiscale neural network URNet for OSA detection by optimizing the deep learning networks and integrating Unet with ResNet. The URNet automatically extracts delicate features from the RR interval of single-lead ECG and processes convolution blocks with different scales by skip connections, so that the network can fuse features collected from both shallow and deep levels. For each OSA segment identification, URNet achieves an accuracy of 90.4%, a sensitivity of 83.3%, a specificity of 94.8% and an F1 of 89.6% on the ApneaECG dataset. The result indicates that our approach provides major improvements compared to the state-of-the-art methods. The URNet model proposed in this study for unobstructive OSA detection has good potential application in daily sleep health.
Keywords: Wearable ECG | Obstructive sleep apnea | Multi-scale neural network | Deep learning
مقاله انگلیسی
43 Smart mask – Wearable IoT solution for improved protection and personal health
ماسک هوشمند – راه حل پوشیدنی اینترنت اشیا برای بهبود حفاظت و سلامت شخصی-2022
The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help the gathering of health data and health-related event detection in real time from the user as well as from their environment. In the larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where the comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research prob- lems for smart respiratory protective equipment are identified in addition to future research di- rections. A Smart Mask prototype was developed with accompanying user application, backend and heath AI to study the concept.
keywords: کووید-۱۹ | محاسبات لبه | اینترنت اشیا | سلامت شخصی | پوشیدنی | COVID-19 | Edge computing | IoT | Personal health | Wearable
مقاله انگلیسی
44 A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه داده‌ها، روش‌ها، چالش‌ ها و جهت‌گیری‌های تحقیقاتی آینده-2022
Rapid developments in network technologies and the amount and scope of data transferred on networks are increasing day by day. Depending on this situation, the density and complexity of cyber threats and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine learning offers various tools and techniques for automating the detection of cyber attacks and for rapid prediction and analysis of attack types. This study discusses the approaches to machine learning methods used to detect attacks. We examined the detection, classification, clustering, and analysis of anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data sets used in each study we examined. We investigated which feature selection or dimension reduction method was applied to the data sets used in the studies. We presented in detail the types of classification carried out in these studies, which methods were compared with other methods, the performance metrics used, and the results obtained in tables. We examined the data sets of network attacks presented as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection
مقاله انگلیسی
45 A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming
طرح شناخت رفتار گاو مبتنی بر یادگیری عمیق برای بهبود مدل‌سازی رفتار گاو در کشاورزی هوشمند-2022
Farming and animal husbandry applications are improvised with the implication of machine learning and artificial intelligence in recent years. The precise estimation, recommendations, and performances are the prime reason for the technology implication. Owing to the modern agri- cultural and animal cultures, this article introduces an innovative Behavior Recognition and Computation Scheme (BRCS) for predicting cow behaviors. The information from the swallowed microchip is processed based on the observed animal action that is used for the forecast. Considering the information to be rectilinear, the distractions and distribution patterns (data) are augmented in identifying and forecasting its behavior. The proposed scheme identifies the pat- terns using a deep recurrent learning paradigm recurrently. This pattern is distinguished for idle and non-idle observations for improving the prediction accuracy. Distinguished data patterns are mapped for the consecutive time and observation data in classifying abnormalities. The proposed scheme’s performance is validated using the metrics accuracy, precision, computing time, and mean error.
keywords: رفتار گاو | تحلیل داده ها | یادگیری عمیق | تشخیص الگو | Cow behavior | Data analysis | Deep learning | Pattern recognition
مقاله انگلیسی
46 A holistic approach to health and safety monitoring: Framework and technology perspective
رویکردی جامع برای نظارت بر سلامت و ایمنی: چارچوب و دیدگاه فناوری-2022
Existing H&S monitoring methods are manual, cumbersome, time consuming and issues with safety compliance and use of PPE remain a concern. With the existing manual H&S processes, there are significant delays and, in some cases, even failure to report incidences, resulting in no or slow improvements to safety. This paper proposes a prototype PPE access monitoring system which combines smart PPE and an indoor/outdoor personnel location monitoring system. The paper also proposes a generic framework to be used for smart gateway services within a manufacturing site to augment and enable smart PPE, separating areas of high and low risk. The prototype automated PPE detection gate presents a practical use of the framework and demon- strates a suitable method for assessing workforce/visitor PPE compliance. Its secondary function is to act as a location waypoint system to support other location tracking methods, identified in the literature and throughout the testing protocol. The system could be further adapted to support augmented personnel within the Operator 4.0 paradigm to improve site safety, monitoring and control.
keywords: اینترنت اشیا | تجهیزات حفاظت فردی | اپراتور 4.0 | انطباق با PPE | چارچوب | RFID | Internet of things | Personal protective equipment | Operator 4.0 | PPE compliance | Framework | RFID
مقاله انگلیسی
47 A jamming attack detection technique for opportunistic networks
یک تکنیک تشخیص حمله پارازیت برای شبکه های فرصت طلب-2022
Opportunistic networks (OppNets) are dispersed in nature, with nodes acting as resource restrictions, with intermittent connectivity. These nodes are subject to various types of attacks, posing a security risk in data transmission. One of the most common attacks that cause jamming among the message forwarding nodes in infrastructure-less networks is Denial of Service (DoS) attack. Most of the methods addressing this type of attack rely on cryptographic algorithms, which are too difficult to implement. In this paper, a novel jamming attack detection technique (JADT) for OppNets, is proposed, which relies on the use of some statistical measures collected from the relay nodes and a prescribed threshold on the packet delivery ratio (PDR) to discover a jamming attack while decrypting the acknowledgement, stopping the message transmission and rebroadcasting the message through a different channel. The proposed JADT is evaluated using the ONE simulator, showing its superiority against the Fuzzy Geocasting mechanism in Opportunistic Networks (F-GSAF) scheme in terms of packet delivery ratio and overhead ratio, under varying TTL and buffer size.
Keywords: Jamming detection | Opportunistic networks | Routing | Statistical information | Energy
مقاله انگلیسی
48 A novel machine learning pipeline to detect malicious anomalies for the Internet of Things
پایپ لاین یادگیری ماشینی جدید برای شناسایی ناهنجاری های مخرب برای اینترنت اشیا-2022
Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices. There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail.
In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.
keywords: IoT | Anomaly detection | Ensemble learning | Predictive analytics
مقاله انگلیسی
49 A solution for water management and leakage detection problems using IoTs based approach
راه حلی برای مشکلات مدیریت آب و تشخیص نشت با استفاده از رویکرد مبتنی بر اینترنت اشیا-2022
Water management, distribution, and consumption are not visualized in real time in conventional systems; this delays the leakage detection process. Nowadays, an increase in the development of smart water- meter trials and demand management requires higher spatial and temporal de- cisions. This paper proposes a solution for the water management and distribution problem. The solution is based on the IoT technology. First, a prototype abstracting the water distribution network (WDN) is developed. Second, sensors are installed on the network to capture the targeted physical quantities such as water pH level, turbidity, and flow rates. Third, sensor network is established to send the readings to Firebase platform. Fourth, an IoT testbed architecture is proposed to comprehensively interface all the IoT modules. Leakage detection scenarios are conducted to sense and warn admins and users to fix it. Application of the proposed system to smart homes would enable monitoring of water quality, measurement of consumption, and detection of leakage. Moreover, it provides an awareness highlight to users about consumption, and a monitoring platform for both users and admins for leakage detection.
keywords: اینترنت اشیا | مدیریت آب | تشخیص نشتی | IOT | Water management | Leakage detection
مقاله انگلیسی
50 A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks
مدل تشخیص نفوذ مه-ابر دو لایه برای شبکه های اینترنت اشیا-2022
The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However, the open deployment environment and the limited resources of IoT devices make them vulnerable to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed extensive experiments using two publicly available datasets to test the proposed approach. Test results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also justified the proposed model in terms of improved service time, lower delay, and optimal energy utilization.
keywords: Fog computing | Intrusion detection | IoT network | Machine learning | Security
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