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Power and Rate Allocation for Energy-Efficient Rate-Splitting Multiple Access via Deep Q-Learning

Power and Rate Allocation for Energy-Efficient Rate-Splittin...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diamanti, Maria Kapsalis, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350370218

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency. © 2023 IEEE.

关键词： Deep Reinforcement Learning (DRL) Energy Efficiency Maximization Rate-Splitting Multiple Access (RSMA)

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Speech Recognition Paradigms: A Comparative Evaluation of SpeechBrain, Whisper and Wav2Vec2 Models 9

Speech Recognition Paradigms: A Comparative Evaluation of Sp...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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Inverse-Designed 16-Channel Time-of-Flight Receiver in 45nm Silicon Photonic Process

Inverse-Designed 16-Channel Time-of-Flight Receiver in 45nm ...

引用

2024 Integrated Photonics Research, Silicon and Nanophotonics, IPR 2024

作者： Rollinson, John Karlicek, Robert Hella, Mona Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

A 16-channel monolithic electronic-photonic receiver for flash time-of-flight LIDAR is demonstrated in 45nm silicon photonic process. Enverse-designed grating array antennas are utilized for efficient, low-noise coupling of free-space light, thus improving signal-to-noise ratio and extending sensing range of the free-space optical receiver. © 2024 The Author (s).© Optica Publishing Group 2024

关键词： Silicon photonics

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Interactive Floor Projection in a Museum for Learning Small Creatures 13

Interactive Floor Projection in a Museum for Learning Small ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Umezu, Nobuyuki Nakagawa, Koki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798350355079

We propose an interactive projection system that uses two projectors and a two-dimensional LiDAR sensor. A mirror was used to reflect a portion of the scanning plane of the sensor to detect the location of the user on the floor and that of the touch. Three-dimensional colored models of small creatures, such as frogs and mantis, were displayed on the floor screen, whereas the front screen on the wall was used to provide mini quizzes and information about the creature. User experiments were conducted at a science museum to evaluate the proposed system. Seventeen participants rated our system at 4.23 on a scale of 1 to 5 in terms of interest, usability, and helpfulness. © 2024 IEEE.

关键词： 2D LiDAR sensor creature shape models mirror quiz silhouette zoom-out

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Utilization Of Data Analytics in Analysing Audio and Crowd Video Content 9

Utilization Of Data Analytics in Analysing Audio and Crowd V...

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9th International Conference on Advanced Computing and communication systems, ICACCS 2023

作者： Singh, Anshy Kumar, Manoj Saxena, Aditi GLA University Dept. of Computer Engineering and Applications Mathura India GLA University Dept. of Electronics and Communication Engineering Mathura India

ISBN: (纸本)9798350397376

The development of growing variety of useful sources of data, the creation of a number of methods for machine learning, and the development of web of things and advanced analytics enable people and policy maker with new tools for predictive services however an interconnected system also has a deficit that will help in reducing building costs and allow new forms of facilities this is a case in point for an applied big data analysis framework of things and smart grid for web applications, this paper presents a summary and description of big data and the internet of the creation of *** paper provides a comparative analysis of audio and crowd video content an intelligent grid case study that shows the high standards of big data analysis *** evaluation processing, analysis and reporting for comprehension and optimization gather and analyzes, of learner data in their contexts and in the condition in which it is generated. © 2023 IEEE.

关键词： Big data

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A Novel General Purpose Processing ASIC for Inertial Sensors 5

A Novel General Purpose Processing ASIC for Inertial Sensors

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5th International Conference on Control, communication and Computing, ICCC 2023

作者： Sruthimol, T. Raghunath, K.P. Daniel, Sanil K Ruma, S.R. College of Engineering Dept. of Electronics and Communication Kerala Trivandrum India ISRO Inertial Systems Unit Kerala India Engineering College of Engineering Dept. of Electronics and Communication Kerala Trivandrum India

ISBN: (纸本)9798350334128

High-performance inertial sensors are widely used for autonomous navigation, guidance and control applications. This requires its electronics to be co-located to the sensor with the best C-SWaP(Cost, Size, Weight, and Power) configuration. Analog circuit-based designs were implemented earlier for such requirements. However, such implementations require manual adjustment of circuit parameters based on sensors and are complex. The digital implementation provides reconfigurability and high performance. However, different kinds of sensors require different processing logic. The typical sensor processing requirements are accomplished by data converters and digital processing logic. The digital processing logic requirement may vary with respect to different kinds of sensors. However, all these requirements can be met by implementing different combinations of the following modules, like IIR, FIR, PID controller and oscillators. Hence, a novel CISC processor with the support of all these logic modules is implemented. The logic modules are fully configured by microcode-based programs and execute in different sequential orders based on sensor requirements. The above implementation has been completely implemented in FPGA and successfully interfaced with various MEMS Gyros and Accelerometers. Further ASIC realization was also carried out on the SCL-180nm Technology node with an area of 2 mm × 2 mm and a CQFP-64 pin package. © 2023 IEEE.

关键词： Three term control systems

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IoT ML Driven Holistic Health Monitoring and Fitness Assessment Empowering Proactive Wellbeing Management

IoT ML Driven Holistic Health Monitoring and Fitness Assessm...

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2024 IEEE Students Conference on engineering and systems, SCES 2024

作者： Mishra, Sachin Singh, Anurag Dept. of Computer Science and Engineering Government Engineering College Raipur Chhattisgarh Raipur India International Institute of Information Technology Dept. of Electronics & Communication Engineering Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350374711

In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proactive management of one's well-being, and enhances overall health outcomes. With the advent of such technologies there are lots of platforms developed. But such platforms are not successful in providing the followings: 1) the ability to monitor all major health vitals in one unified application, & 2) the provision of a holistic health score derived from these non-invasive vitals for fitness measurement. In response, this paper presents a novel solution featuring: 1) An IoT-ML enabled platform for self-monitoring of important health vitals, and 2) A fitness assessment score derived through ML algorithm using the health vitals of a person. By integrating data on various health parameters including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models are developed to analyze this data and predict the fitness score that accurately reflects an individual's overall health and fitness level. The performance of the proposed estimation model is evaluated using R2 and mean square error (MSE) and the best value is achieved using the XGBoost regressor of values 0.967 and 0.0033. This solution empowers individuals to take control of their well-being and improve their overall quality of life. Overall, the integration of non-invasive methods and IoT sensors for health vitals monitoring and fitness assessment presents a novel approach with significant potential to revolutionize health care. By empowering individuals with real-time insights and a comprehensive fitness score, this solution facilitates informed decision-making and enables individuals to proactively manage their health and well-being, ultimately enhancing their overall quality of life. © 2024 IEEE.

关键词： Electronic health record

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Multi-Task Bias-Variance Trade-Off Through Functional Constraints 48

Multi-Task Bias-Variance Trade-Off Through Functional Constr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cervino, Juan Bazerque, Juan Andrés Calvo-Fullana, Miguel Ribeiro, Alejandro University of Pennsylvania Dept. of Electrical and Systems Engineering Philadelphia United States University of Pittsburgh Dept. of Electrical and Computer Engineering Pittsburgh United States Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona Spain

ISBN: (纸本)9781728163277

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the intrinsic similarity across data sources to aid in the learning process for each individual domain. In this paper we draw intuition from the two extreme learning scenarios - a single function for all tasks, and a task-specific function that ignores the other tasks dependencies - to propose a bias-variance trade-off. To control the relationship between the variance (given by the number of i.i.d. samples), and the bias (coming from data from other task), we introduce a constrained learning formulation that enforces domain specific solutions to be close to a central function. This problem is solved in the dual domain, for which we propose a stochastic primal-dual algorithm. Experimental results for a multi-domain classification problem with real data show that the proposed procedure outperforms both the task specific, as well as the single classifiers. © 2023 IEEE.

关键词： Constrained Learning Multi-task Learning

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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue 5

Innovative Approaches to Dental Age Estimation: A Comprehens...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shyni, I. Domilin Sheeja Herobin Rani, C. Sheeba, I. Rexiline Mahizha, S.I. Dmi Engineering College Dept. Of Computer Science and Engineering Aralvoimozhi India St.Xavier's Catholic College of Engineering Dept. Of Electronics and Communication Engineering Nagercoil India Sathyabama Institute of Technology and Science Dept. Of Electronics and Communication Engineering Chennai India St.Xavier's Catholic College of Engineering Dept. Of Information Technology Nagercoil India

ISBN: (纸本)9798350389609

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age. © 2024 IEEE.

关键词： Convolutional neural networks

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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