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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 37219 条记录，以下是1751-1760 订阅

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Going Beyond Backscatter: Rethinking Low-Power Wireless Transmitters using Tunnel Diodes 23

Going Beyond Backscatter: Rethinking Low-Power Wireless Tran...

引用

29th Annual international conference on Mobile computing and Networking, MobiCom 2023

作者： Shah, Moteen Amin Bijoy, Adithya Gulati, Manoj Yan, Wenqing Varshney, Ambuj Electronics and Telecommunications Veermata Jijabai Technological Institute Maharashtra Mumbai60022429 India Computer Science and Engineering Indian Institute of Technology Delhi Delhi60032730 India School of Computing National University of Singapore Singapore60017161 Singapore Uppsala University Uppsala60003858 Sweden

ISBN: (纸本)9781450399906

A stark disparity exists in the energy consumption for performing transmissions and the tasks of sensing and processing in wireless embedded systems. We present our early work to design a novel transmitter that enables transmissions at a similar energy consumption as other tasks in wireless embedded systems. In particular, the proposed transmitter does not require a carrier emitting device, which is essential to support backscatter transmitters, but has also limited their widespread deployment. The proposed transmitter exploits the capability of tunnel diode oscillators to function as a low-power, self-oscillating mixer. This property enables us to mix a weak baseband signal with a locally generated carrier signal at a peak power consumption below 100 microwatts. Nevertheless, the tunnel diode oscillator trades off the stability for low energy consumption leading to poor link reliability. In this study, we investigate error correction codes to increase link reliability. Our experiments demonstrate the potential of the transmitter to support short-range transmissions with a low energy consumption and enhanced link reliability. © 2023 ACM.

关键词： embedded systems

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ISED 2023 - international conference on Intelligent systems and embedded Design

ISED 2023 - International Conference on Intelligent Systems ...

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11th international conference on Intelligent systems and embedded Design, ISED 2023

ISBN: (纸本)9798350318159

The proceedings contain 48 papers. The topics discussed include: a computational analysis of drug-expanse predictions from biomaterials using ensemble model for healthcare applications;real-time object detection and tracking using deep learning;advances in facial emotion recognition: leveraging machine learning for human emotion analysis;cybersecurity audits for emerging and existing cutting edge technologies;cross layer design for TCP optimization through flow control and adaptive modulation and coding;a systematic deep learning approach to forecast Nifty50 index trend;secure cloud storage access framework using blockchain technology;and dual-constraint based task scheduling and secure offloading in triune layered fog assisted IoT environment.

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Machine Learning-Enhanced Microfluidic SIW Sensor for Water-in-Fuel Detection applications 14

Machine Learning-Enhanced Microfluidic SIW Sensor for Water-...

引用

14th international conference on Information and Communication systems, ICICS 2023

作者： Altakhaineh, Amjaad T. Alia'afreh, Saqer S. Mutah University Electrical Engineering Department Alkarak Jordan Mutah University Electrical Engineerng Department Alkarak Jordan

ISBN: (纸本)9798350307870

This paper presents a half-mode substrate-integrated waveguide (HMSIW) sensor with a microfluidic integrated channel for detecting microliter water-in-diesel at 7.1 GHz. A microfluidic channel is embedded into the HMSIW structure, allowing interaction with the fields of TE103 mode. The sensor's performance is assessed by following the resonance frequency fluctuations as water concentration varies in a 65 mumathrm{L} sample using an examination of dielectric permittivity behavior. The resonance frequency is highly sensitive, shifting by 6.4 MHz per 0.05% water content. A back-propagation artificial neural network (BP-ANN) model is also deployed to allow cognitive capabilities, attaining a stunning detection range down to 0.01%. This sensor offers a potential option for real-time monitoring and quality control in fuel systems because to its multipurpose capabilities. © 2023 ieee.

关键词： Microwave sensors

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Exploring the Performance of You only Look Once Model for Object Detection in VQA Images

Exploring the Performance of You only Look Once Model for Ob...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Asudani, Deepak Suresh Nagwani, Naresh Kumar Singh, Pradeep National Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India

ISBN: (纸本)9798331517953

Identifying objects is among the most crucial and complex problems in computer vision. The effectiveness of object detection tasks has significantly increased due to advancements in deep learning architectures. Object detectors are classified into single-stage (SSD) and two-stage (TSD). SSD model architecture evaluates the spatial region proposals in a single pass. TSDs employ a more complex architecture to focus on selective region proposals. Object detection is an essential component of visual question answering (VQA), which requires algorithms to respond to text-based inquiries about images. While many object detection algorithms are available, real-time application is still somewhat difficult. This paper explores the object detection approaches for VQA systems through a comparative analysis of various YOLO variants. The study compares the effectiveness of YOLOv8s and YOLOv8n architectures against TSD approaches in detecting object positions with bounding boxes. Experiments were conducted on the MS-COCO and two versions of the PASCAL-VOC (2007 and 2012) datasets. The paper outlines the data preprocessing steps required to convert source datasets to the YOLO model format. The experimental evaluations on the PASCAL-VOC dataset indicate that the TSD Faster-RCNN model, using ResNet101 pretrained architecture, achieves the highest mAP of 78.80. In contrast, the SSD YOLOv8s model achieves the highest mAP of 89.63 using the CSPDarkNet53 model architecture. YOLO is essential for real-time object detection, allowing VQA systems to process images in a single forward pass. The study concludes that the SSD YOLOv8s model efficiently identifies objects and generates bounding boxes with higher accuracy compared to other models. © 2024 ieee.

关键词： real time systems

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Image Encryption Through Aperiodic Josephus Permutation And Novel Cyclic Shift Operation 3

Image Encryption Through Aperiodic Josephus Permutation And ...

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3rd international conference on Advances in computing, Communication, embedded and Secure systems, ACCESS 2023

作者： Vithayathil, Asha J. Sreekumar, A. Cochin University of Science and Technology Department of Computer Applications Cochin India

ISBN: (纸本)9798350346169

In recent years, chaos based cryptography has become a prevalent and efficient way to secure digital images because of the similarities between chaotic properties and the traits needed for encryption. This paper proposes an image encryption algorithm combining a chaotic map, Josephus problem, cyclic shift operation, and XOR operation. The proposed encryption procedure follows the traditional permutation-substitution or confusion-diffusion structure. Here, the Henon map and the novel key generation phase generate image-sensitive chaotic streams, which are used for permutation and diffusion. The aperiodic Josephus permutation and bit-level chaotic cycle shift method accomplish the permutation stage by altering the position and value of each pixel. And this proposed permutation thwarts statistical cryptanalysis by dropping the correlation between neighboring pixels to approximately equal zero. The substitution stage is achieved with modulus and XOR operations on the scrambled image and the chaotic matrix. We compare the proposed method with other recent image encryption algorithms in the simulation experiment and security analysis, and the results confirm that the proposed method has better performance and higher security. © 2023 ieee.

关键词： Cryptography

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A Comprehensive Study of Available and Prospective Object Detection Methodologies 1

A Comprehensive Study of Available and Prospective Object De...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350353648

Visual recognition and recognition of objects have created a new field of relevance in modern times, posing severe issues for computer vision. Object detection technology is increasingly being used in a variety of applications, the most noteworthy of which include surveillance systems, self-driving vehicles, and pedestrian identification. One of the most significant and difficult issues is visual identification and obj ect detection, which has enticed an area of significance in modern years. The most well-known applications of object detection include surveillance, self-driving automobiles, pedestrian detection, and other areas of daily life. The primary objective of visual object detection is to recognized objects that fit into a certain targeted object with excellent navigation in a real scene or input image, as we know to assign a specific class label to each object instance due to the rapid growth of deep learning models, the performance of object detectors has significantly boosted, deep learning-based detection algorithms have been extensively investigated once again. This paper provides a comprehensive overview of the most recent advancements in visual object identification using deep learning and also describes a huge corpus of recent research and used it to analyses conventional and modern obj ect detectors. Here this study discussion the backbone architecture for obj ect recognition, followed by a methodical deconstruction of existing learning algorithms. Some important datasets and metrics for objects detection are also explored. Finally, this paper describes object detection applications and suggests various future directions. © 2024 ieee.

关键词： Intelligent systems

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Data Fusion Using Ultra Wideband time-of-Flight Positioning for Mobile Robot applications

Data Fusion Using Ultra Wideband Time-of-Flight Positioning ...

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ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Lima, Jose Pinto, Andre F. Ribeiro, Francisco Pinto, Milena Pereira, Ana I. Pinto, Vitor H. Costa, Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia Braganca Portugal Inst Politecn Braganca Lab Sustainabil & Technol Mt Reg SusTEC Braganca Portugal INESC Technol & Sci Porto Portugal Univ Porto Fac Engn SYSTEC DIGI2 Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Fac Engn ARISE Rua Dr Roberto Frias P-4200465 Porto Portugal Fed Ctr Technol Educ Celso Suckow Fonseca CEFET R Fonseca Brazil Univ Porto Fac Engn ECE Dept Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9798350301212

Self-localization of a robot is one of the most important requirements in mobile robotics. There are several approaches to providing localization data. The Ultra Wide Band time of Flight provides position information but lacks the angle. Odometry data can be combined by using a data fusion algorithm. This paper addresses the application of data fusion algorithms based on odometry and Ultra Wide Band time of Flight positioning using a Kalman filter that allows performing the data fusion task which outputs the position and orientation of the robot. The proposed solution, validated in a real developed platform can be applied in service and industrial robots.

关键词： Data fusion

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Enhanced Decision Support System for Financial Risk Assessment Using Hybrid Fuzzy Logic and Machine Learning

Enhanced Decision Support System for Financial Risk Assessme...

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2024 international conference on Computational Intelligence and computing applications, ICCICA 2024

作者： Kaur, Taranjot Rani, Kavita Thakur, Payal Talwandi, Navjot Singh Ghg Khalsa College Of Education Dept Of Computer Applications Punjab India Piet Dept Of Cse Punjab India Chandigarh University Dept Of Cse Punjab India

ISBN: (纸本)9798350306446

This paper proposes an advanced framework for financial risk assessment leveraging a synergistic blend of fuzzy logic and machine learning techniques. In today's volatile financial markets, accurate risk evaluation is imperative for effective decision-making. Traditional methods often fall short in handling uncertainty and complexity inherent in financial data. Hence, this study introduces a novel approach integrating fuzzy logic for handling imprecise information and machine learning algorithms for predictive modeling. The proposed system employs a hybrid model that amalgamates fuzzy inference systems with ensemble learning, harnessing the strengths of both paradigms. Fuzzy logic aids in capturing vague and qualitative aspects of financial risk assessment, while machine learning algorithms like random forests or gradient boosting enhance predictive accuracy by learning from historical patterns and trends. Furthermore, the study presents a comprehensive evaluation using real-world financial datasets to demonstrate the efficacy and superiority of the proposed hybrid approach compared to conventional methods. Performance metrics such as accuracy, precision, and recall are utilized to validate the system's effectiveness in risk assessment across different market scenarios. This research contributes to the burgeoning field of financial risk management by providing a robust decision support system capable of handling uncertainties inherent in financial markets, thereby assisting stakeholders in making informed and proactive risk mitigation strategies © 2024 ieee.

关键词： Decision support systems

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Photovoltaic Energy Production Forecasting and Operational Analytics: A real-World Study

Photovoltaic Energy Production Forecasting and Operational A...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Alexakos, Alexandros Amaxilatis, Dimitrios Zaroliagis, Christos Univ Patras Dept Comp Engn & Informat Patras Greece SparkWorks Ltd Galway Ireland Comp Technol Inst Patras Greece Press Diophantus Patras Greece

ISBN: (纸本)9781665416474

Photovoltaic energy production is one of the major parts for the shift of our societies away from fossil fuels. Their irregular nature however poses a challenge for electricity grid managers. PV energy production forecasting and monitoring is extremely important to guarantee that the grid will be able to supply consumers at all times and without any unexpected interruptions. Our goal is to provide build a system that can effectively monitor all aspects of the operation of a PV power station and provide owners, facility managers and power grid administrators with high quality forecasts and operational insights. To better understand and evaluate our work, we test our system in a real-world case study from Arta, Greece, and benchmark the effectiveness of both mathematical models and machine learning based solutions with regards to both precision, consistency and trustworthiness.

关键词： Monitoring Internet of things embedded systems Solar panels Photovoltaic systems Smart grids Forecasting Solar power output Machine Learning Random Forests Feature selection Prediction accuracy Renewable energy system

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Controlling the Reliability of Measuring Channels in Automated Monitoring systems

Controlling the Reliability of Measuring Channels in Automat...

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2024 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2024

作者： Krasnov, Andrey N. Kolovertnov, Gennady Yu. Prakhova, Marina Yu. Ufa State Petroleum Technological University Department Of Automation Telecommunications And Metrology Ufa Russia

ISBN: (纸本)9798350395013

The development of automation systems for the production facilities of the oil and gas sector comprises several paths, one of which is the introduction of automated monitoring systems that can perform technical diagnostics of the facility in real-time. In many cases, using these systems can improve the reliability of the facility's operations and, in some cases, it facilitates the transition to low-staffed operations. The efficiency of these systems depends on the reliability of the data transferred over measuring channels. This article presents a reliability-control algorithm based on the direct and reverse facility models. It can identify partial probe failures, i.e. the loss of metrological reliability, based on the invariance tests of some parameters, as well as the changes in the parameters of the facility considered. Model construction uses the statistics obtained in normal, pre-emergency, and emergency operating modes of a fuel pump station. Model verification confirmed its adequacy. Using the algorithms considered as additional measuring channel control tools is recommended for any facility. © 2024 ieee.

关键词： Reliability

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