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Sign Language Detection in real-time applications

Sign Language Detection in Real-Time Applications

引用

2024 international conference on computing and Intelligent reality Technologies, ICCIRT 2024

作者： Milinda, Renata Rachael Sara Ramsiej, Barrister Yasmin, Reshma Ambeth Kumar, V.D. Sekar, K. Anna University India Dept. of Artificial Intelligence and Machine Learning Anna University India Dept. of Computer Engineering Mizoram University Aizawl India Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331510299

Individuals with hearing and speech impairments often rely on gesture language for communication, but a lack of widespread understanding of this language among others creates significant barriers. A machine learning model that recognizes hand signals and converts them into English can help bridge this gap, facilitating communication between hearing and deaf individuals. Existing non-verbal communication identification systems, while leveraging computational learning and AI models for both single- and double-handed gestures, generally lack real-time capabilities. This research proposes a live symbolic language identification platform that uses a webcam to build a regional gesture communication dataset and TensorFlow for cross-domain learning. Despite a smaller dataset, the system targets high accuracy in recognition. Advanced technologies, including computer vision and deep learning, are applied to improve communication for deaf individuals by developing accessible technical applications and platforms. The proposed model, utilizing TensorFlow and OpenCV, aims to identify commonly used American Sign Language gestures in real time with accuracy and efficiency, including signs such as "hello, " "thanks, " "bye, " "yes, " "no, " "dad, " and "mom". © 2024 ieee.

关键词： Adversarial machine learning

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Optimal real-time Task Allocation in Heteregeneous Multi-Core embedded systems

Optimal Real-Time Task Allocation in Heteregeneous Multi-Cor...

引用

ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： David Doose Youcef Bouchebaba Alfonso Mascarenas Gonzalez ONERA Toulouse France

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

In response to the increasing complexity of real-time embedded software, driven by the need for intelligent computation in constrained environments, multicore architectures have emerged as a promising solution. The challenge lies in to choose an effective mapping of the real-time tasks to the various computational resources of these embedded boards, while ensuring real-time constraint satisfaction. To address this problem, our approach rests on two pillars. The first is a domain-specific language designed to capture hardware and software characteristics, constraints, and criteria in a clear and unambiguous manner. The second is a solver method based on Satisfiability Modulo Theories solver augmented with a lazy theory to handle real-time aspects. This method allows us to synthesize mappings that respect temporal constraints and optimize specific criteria, such as the power consumption of the embedded board.

关键词： Power demand embedded systems Multicore processing Computer architecture real-time systems Hardware Resource management Optimization embedded software Domain specific languages

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Movie Recommendation System Based on Emotion Detection Using Machine Learning Techniques

Movie Recommendation System Based on Emotion Detection Using...

引用

2024 ieee international conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Vaishnavi, S.R. Sreelakshmi, S. Anu Prabha, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The face is a critical perspective in predicting human feelings and moods. More frequently than not human senti-ments are extricated with the utilization of the camera. Various applications are being made based on the location of human sentiments. A few applications of feeling revelation are trade notice suggestion, e-learning, mental clutter, sadness disclosure, criminal conduct discovery, etc. This paper presents a novel real-time emotion-based movie recommendation system that combines computer vision, deep learning, and image processing strategies. The system coordinating OpenCV near DeepFace for effective emotion examination utilizing webcam input, giving clients personalized movie recommendations based on their recognized enthusiastic states. The system commences by using OpenCV to capture real-time webcam feeds and utilizes a Haar-Cascade classifier for facial discovery. The recognized faces are analyzed for prevailing feelings utilizing the DeepFace library, empowering exact feeling distinguishing proof. Within the recommendation stage, the system joins content-based filtering by processing a movie dataset utilizing TF-IDF. Genres and plot keywords serve as features for building the TF-IDF matrix. Cosine similarities between the user's emotion vector and relevant movie genres are then calculated, coming about in a list of personalized movie rec-ommendations. Index Terms-FER, OpenCV, DeepFace, Haar-Cascade Algorithm, Content-Based Filtering, TF-IDF © 2024 ieee.

关键词： Emotion recognition Visualization Webcams Face recognition Motion pictures real-time systems Libraries

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Blockchain Sharding in ieee 802.11ax Networks 20

Blockchain Sharding in IEEE 802.11ax Networks

引用

20th Annual ieee international conference and Expo on real time Communications (RTC)

作者： Hegazy, Rana Moghadam, Nadieh Univ San Diego Dept Elect Engn San Diego CA 92110 USA

ISBN: (纸本)9798350387629;9798350387612

Blockchain technology has extended to secure systems and applications beyond cryptocurrency. Sharding the Blockchain technique increases the system throughput and decreases the delay while enhancing the system security. In this paper, the Medium Access Control protocol for the wirelessly connected Internet of Things devices through ieee 802.11ax is updated to include the validation process required by Ethereum sharding. The simulation results, where nodes compete to transmit their data, align with the theoretical equations presented in the paper. Lastly, the results show linear enhancement in the performance of the system as the number of shards increases.

关键词： Wireless communications Blockchain Ethereum Sharding ieee 802.11ax

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Two-Dimensional Reverse time Migration for Multi-Input Multi-Output System by Shot Correlation Method with Noise Effect

Two-Dimensional Reverse Time Migration for Multi-Input Multi...

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ieee international conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic systems (COMCAS)

作者： Wang, Huan Zhang, Yuxian Tong, Mei Song Huang, Zhixiang Feng, Naixing Anhui Univ Sch Elect & Informat Engn Hefei 230601 Anhui Peoples R China Tongji Univ Coll Elect & Informat Engn Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China

ISBN: (纸本)9798350348194;9798350348187

In this paper, we propose a shot correlation reverse time migration (SC-RTM) method for multiple-input-multiple-output (MIMO) ground penetrating radar (GPR) imaging to improve image contrast. In GPR applications, noise is unavoidable in radar echo signals and can cause artifacts in the subsurface reconstructed image. Since in general the correlation among noise signals is weaker than that in target signals, a shot correlation image operator is proposed to integrate all shot images and arrive at the final reconstructed image with artifacts suppression and resolution improvement. In numerical experiments, to test the performance of artifacts suppression, we add different noise levels in the synthetic GPR data. It is shown that the noise is effectively suppressed and the contrast between targets and artifacts is improved in reconstructed images of the SC-RTM method by more than 15 dB. The measured MIMO GPR echo signals with significant noises are successfully processed by the SC-RTM method to yield high contrast subsurface images.

关键词： ground-penetrating radar (GPR) reverse time migration (RTM) shot correlation multiple-input-multipleo-utput (MIMO)

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The Dynamic Window-Based Scheduling Framework For Complex Wireless Sensor Networks 2

The Dynamic Window-Based Scheduling Framework For Complex Wi...

引用

2nd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Bargavi, S. K. Manju Mahesh, T.R. Department of Computer Science and It Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The dynamic window-based scheduling is a popular scheduling algorithm used to manage system resources efficiently. It is used in many different types of systems including distributed systems, embedded systems, and real-time systems. This scheduling algorithm works by assigning a window of time to a process or task. The window is then moved through the system, allowing each process or task to use the resources that it needs within the window. The dynamic window-based scheduling algorithm is advantageous because it allows the system to use the resources efficiently. By assigning a window of time to each process or task, the system can ensure that each process or task has an equal amount of time to execute. This also allows the system to prioritize tasks, so that processes or tasks that have higher priority will have a larger window of time to use the resources. Another advantage of the dynamic window-based scheduling algorithm is that it can help reduce latency and improve system performance. By assigning a window of time to each process or task, the system can ensure that each task is able to execute within the window. This reduces latency and improves system performance by minimizing the amount of time that tasks are waiting for resources. Furthermore, the dynamic window-based scheduling algorithm can also help improve system scalability. © 2023 ieee.

关键词： real time systems

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Enhancing Networked embedded systems Using Deep Learning Techniques 3

Enhancing Networked Embedded Systems Using Deep Learning Tec...

引用

3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Asha, K. Kumar, Ritesh Fiza, Samreen Department of Life Sciences Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

Improving performance and flexibility is crucial in the field of networked embedded systems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creating a custom deep learning architecture tailored to the specific needs of distributed embedded systems. This strategy makes use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) to evaluate real-time data streams, expedite decision-making, and intelligently adapt to ever-changing environments. In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting. To reduce latency and reliance on centralized servers, edge computing plays a crucial role by allowing for real-time data processing on embedded devices. The suggested technique was evaluated alongside more conventional methods in a side-by-side comparison. Imaginary numbers were utilized for demonstration purposes. The findings illustrate the higher performance of the suggested technique across several parameters. When compared to baseline deep learning methods like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Federated Learning, Transfer Learning, and time Series Analysis with Deep Learning, the proposed method shows marked improvements in accuracy, latency, energy efficiency, robustness, security, scalability, and resource utilization. Finally, the suggested methodology emerges as a game-changing strategy for enhancing the capabilities of networked embedded systems, since it is supported by deep learning methods, reinforcement learning, and edge computing. Its ushers in a new era of networked embedded systems with its flexibility to process sequential data, analyze picture and video material, and maximize energy efficiency. The results of the comparison study validate the superior

关键词： Energy efficiency

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Framework for the Analysis and Configuration of real-time OpenMP applications 21

Framework for the Analysis and Configuration of Real-Time Op...

引用

ieee 21st international conference on Industrial Informatics (INDIN)

作者： Carvalho, Tiago Pinho, Luis Miguel Samadi, Mohammad Royuela, Sara Munera, Adrian Quinones, Eduardo Polytech Inst Porto Porto Portugal Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781665493130

High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.

关键词： Cyber-Physical systems real-time Timing Analysis Task-to-thread Mapping

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Improved the Accuracy of Indoor Positioning at the Meter Level in Large and Complex Environments Through AI-Powered Sensor Fusion

Improved the Accuracy of Indoor Positioning at the Meter Lev...

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2024 ieee international conference on computing, ICOCO 2024

作者： Asaduzzaman, Mina Tan, Kim Geok Tan, Choo Peng Sayeed, Md. Shohel Hossain, Ferdous Hasan, Md. Abu Sayed Mahfuz Faculty of InformationScience and Technology Multimedia University Melaka Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798331530303

Indoor positioning systems (IPS) continue to encounter significant challenges in achieving meter-level accuracy, particularly in large and intricate environments such as airports, hospitals, and industrial sites. Despite substantial advancements in technologies like Wi-Fi Fine time Measurement (FTM), Ultra-Wideband (UWB), and 5G, their scalability and precision often suffer in dynamic, obstacle-laden settings. To enhance accuracy and adaptability, this study presents an innovative AI-driven sensor fusion technique that integrates machine learning models with various positioning technologies. This study achieves significant improvements in positioning accuracy through dynamic sensor input adjustments and real-time environmental awareness. Additionally, this study also includes results from a thorough evaluation of the system's effectiveness across diverse practical applications, while exploring its potential uses within the Internet of Things (IoT) framework. ©2024 ieee.

关键词： time measurement

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Sensor Classifications and Their applications in IoT systems

Sensor Classifications and Their Applications in IoT Systems

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2023 ieee international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Sajja, Komalsai Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Vasistha IoT Lab Brics Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350340129

The Internet of Things (IoT), which connects various systems and devices to create more innovative environments, has completely changed how we interact with technology. Sensors are essential to the success of IoT systems because they are vital in gathering and analyzing real-time data from the physical environment. This paper comprehensively analyses sensor classifications and their applications in IoT systems. It explores the different types of sensors commonly used in IoT, their functionality, and their suitability for various IoT-enabled intelligent applications. This paper also provides valuable insights for IoT designers and researchers in selecting appropriate sensors and developing more efficient and reliable intelligent systems for the future. © 2023 ieee.

关键词： Internet of things

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