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A Systematic Review on Automatic Speech Recognition for Odia Language 1

引用

11th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2023

作者： Mishra, Namita Dash, Satya Ranjan Parida, Shantipriya Prasad, Ravi Shankar School of Computer Engineering KIIT University Bhubaneswar India School of Computer Applications KIIT University Bhubaneswar India Silo AI Helsinki Finland Idiap Research Institute Martigny Switzerland

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Automatic speech recognition (ASR) systems need to be trained on large data sets of audio and its transcript of a particular language to produce more accurate results. In our daily life, the ASR system in our smartphones like Siri is very helpful for people who try to communicate in English;however, the same system is very less helpful to those who speak the "low resource" languages. From the previous analysis, it is found that more than 10% of searches are made by voice for the English language and also the majority of them are by Smartphones. Keeping all these aspects in mind, a successful ASR system will be beneficial for all to communicate, and also the life of the language would be prolonged. Else in due course of time, people will face a lot of problems communicating;as a result, widely spoken language (English) may replace Odia, and the Odia language will be abolished. No such work done on building an ASR system for Odia speech. In our paper, we plan to prepare an Odia speech corpus and also plan to use machine learning and deep learning techniques and various online toolkits, resources, and language models helpful to prepare an Odia ASR. Till now no work done in Odia ASR, so working in Odia ASR will be of great help to other researchers as well as society. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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Onboard real-time Object Detection for UAV with embedded NPU 11

Onboard Real-Time Object Detection for UAV with Embedded NPU

引用

11th IEEE Annual international conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2021

作者： Chen, Long Hu, Jingyi Li, Xuanfu Quan, Fengyu Chen, Haoyao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Huawei Technology Co. Ltd Department of HiSilicon Research China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665425278

Unmanned aerial vehicles (UAVs) endowed with the function of computer vision are widely applied. there is an urgent need to implement real-time object detection. However, the limited memory and computing capacity of current embedded devices prevent the deployment of deep learning on UAVs. therefore, this paper proposes an efficient onboard object detection system based on an embedded NPU device. It can be deployed on a UAV to perform real-time scenario analysis. Firstly, a deep-learning network structure based on YOLOV3-Tiny is designed for object detection. then it is compressed by a novel pruning strategy to obtain a 'slim' object detector. Finally, a detection-reinitialization mechanism is introduced to realize the robust output of detection. the experimental results demonstrate that our model and algorithm achieve efficient performance of detection compared to the unoptimized benchmark. the proposed model achieves up to 0.591 mAP with a 6M parameter size. the complete system is tested in both simulation and real environments of the aerial manipulator grasping task. the effective detection frame ratio reaches 90%, and FPS achieves about 25. © 2021 IEEE.

关键词： Object detection

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Consolidating and Optimizing embedded Processor IP Blocks for Area, Power, and Sustainability

Consolidating and Optimizing Embedded Processor IP Blocks fo...

引用

international Green computing conference

作者： Boisy Gene Pitre Martin Margala Kevin J. Phillipson Michael M. Rywalt School of Computing & Informatics University of Louisiana at Lafayette Lafayette Louisiana USA Department of Electrical & Computer Engineering University of Florida Gainesville Florida USA

ISBN: (数字)9798331507862

ISBN: (纸本)9798331507879

the growth of computing continues to place power demands on an ever-stressed energy grid. embedded systems are a part of that demand. Focusing on the architecture of the System-On-Chip (SoC) is an area of interest for power optimization. Intellectual Property (IP) blocks that provide support functionality for SoC designs are often licensed from vendors who provide little or no visibility into their inner workings, requiring reliance on vendor-supplied metrics. this work proposes TurbOS, a small footprint open-source operating system (OS) for reducing required resources within IP blocks. TurbOS utilizes the Turbo9 microprocessor, and is just 20% the size of the popular FreeRTOS operating system, while providing the same functionality. For certain applications, the number of IP blocks can be reduced without loss of functionality.

关键词： Measurement Power demand Microprocessors Computer architecture Intellectual property Multitasking real-time systems System-on-chip Sustainable development Optimization

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Scaling Interprocedural Static Data-Flow Analysis to Large C/C++ applications An Experience Report 38

Scaling Interprocedural Static Data-Flow Analysis to Large C...

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38th European conference on Object-Oriented Programming, ECOOP 2024

作者： Schiebel, Fabian Sattler, Florian Schubert, Philipp Dominik Apel, Sven Bodden, Eric Fraunhofer Institute for Mechatronic Systems Design IEM Paderborn Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Heinz Nixdorf Institute Paderborn Germany Paderborn University Department of Computer Science Heinz Nixdorf Institute Germany Fraunhofer IEM Paderborn Germany

ISBN: (纸本)9783959773416

Interprocedural data-flow analysis is important for computing precise information on whole programs. In theory, the popular algorithmic framework interprocedural distributive environments (IDE) provides a tool to solve distributive interprocedural data-flow problems efficiently. Yet, unfortunately, available state-of-the-art implementations of the IDE framework start to run into scalability issues for programs with several thousands of lines of code, depending on the static analysis domain. Since the IDE framework is a basic building block for many static program analyses, this presents a serious limitation. In this paper, we report on our experience with making the IDE algorithm scale to C/C++ applications with up to 500 000 lines of code. We analyze the IDE algorithm and its state-of-the-art implementations to identify their weaknesses related to scalability at both a conceptual and implementation level. Based on this analysis, we propose several optimizations to overcome these weaknesses, aiming at a sweet spot between reducing running time and memory consumption. As a result, we provide an improved IDE solver that implements our optimizations within the PhASAR static analysis framework. Our evaluation on real-world C/C++ applications shows that applying the optimizations speeds up the analysis on average by up to 7×, while also reducing memory consumption by 7× on average as well. For the first time, these optimizations allow us to analyze programs with several hundreds of thousands of lines of LLVM-IR code in reasonable time and space. © Fabian Schiebel, Florian Sattler, Philipp Dominik Schubert, Sven Apel, and Eric Bodden

关键词： Scalability

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Water Quality Evaluation using Machine Learning

Water Quality Evaluation using Machine Learning

引用

international conference on Communication and Electronics systems (ICCES)

作者： Pongiannan R.K. Akash Sivakumar Shivain Kohli Poornima P.U. Lourde Jame S. Pemila. M Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Dept. of Electrical and Electronics Engg SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Maintaining the water quality is vital for environmental sustainability,public health, and effective resource management. this research study outlines the development and deployment of a real-time water quality monitoring system., highlighting the essential parameters like pH., turbidity,and hardness. Machine learning algorithms,such as Random Forest(RF),K-Nearest Neighbor (KNN),and Artificial Neural Networks (ANN) are utilized to improve prediction accuracy and enable real-time water quality assessment. the water quality managements system has two parts namely the embedded hardware unit with sensor modules, and a machine learning models offers a scalable,cost-effective solution for real-time water monitoring,significantly improving the detection of contamination and enabling efficient water management across various applications.

关键词： Machine learning algorithms Accuracy Water quality Machine learning Prediction algorithms real-time systems Water pollution Monitoring Random forests Contamination

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Dispatching Deadline Constrained Jobs in Edge computing systems

Dispatching Deadline Constrained Jobs in Edge Computing Syst...

引用

international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Shaik Mohammed Salman Alessandro Vittorio Papadopoulos Saad Mubeen thomas Nolte Mälardalen University Västerås Sweden

the edge computing paradigm extends the architectural space of real-time systems by bringing the capabilities of the cloud to the edge. Unlike cloud-native systems designed for mean response times, real-time industrial embedded systems are designed to control a single physical system, such as a manipulator arm or a mobile robot, that requires temporal predictability. We consider the problem of dispatching and scheduling of jobs with deadlines that can be offloaded to the edge and propose DAL, a deadline-aware load balancing and scheduling framework that leverages the availability of on-demand computing resources along with an on-arrival dispatching scheme to manage temporal requirements of such offloaded applications. the evaluation indicates that DAL can achieve reasonably good performance even when execution times, arrival times, and deadlines vary.

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EffiE: Efficient Convolutional Neural Network for real-time EMG Pattern Recognition System on Edge Devices

EffiE: Efficient Convolutional Neural Network for Real-Time ...

引用

international IEEE/EMBS conference on Neural Engineering, CNE

作者： Jimmy Lu Philip Liang Jin Chul Rhim Xiaorong Zhang Zhuwei Qin Lowell High School San Francisco CA School of Engineering San Francisco State University San Francisco CA

With the advancement of deep learning (DL) technologies, applying DL methods to processing surface electromyo-graphic (sEMG) signals for movement intent recognition has gained increasing interest in the research community. Compared to conventional non-DL methods commonly used for EMG pattern recognition (PR), DL algorithms have the advantage of automatically extracting sEMG features without the cumbersome manual feature engineering step and are especially effective in processing sEMG signals collected from 1-dimentional (1D) or 2D sensor arrays. However, a key challenge to the deployment of DL methods in sEMG-controlled neural-machine interface (NMI) applications (e.g., myoelectric controlled prostheses) is the high computational cost associated with DL algorithms (e.g., convolutional neural network (CNN)) since most NMI applications need to be implemented on resource-constrained embedded computer systems and have real-time requirements. In this paper, we designed and implemented EffiE - an efficient CNN for real-time EMG PR system on edge devices. the development of the EffiE system integrated several strategies including a deep transfer learning strategy to adaptively and quickly update the pre-trained CNN model based on the user's newly collected data on the edge device, and a deep learning quantization method that can dramatically reduce the memory consumption and computational load of the CNN model without sacrificing the model accuracy. the proposed EffiE system has been implemented on a Sony Spresense 6-core microcontroller board as a working prototype for real-time NMIs. the embedded NMI prototype has integrated input/output interfaces as well as efficient memory management and precise timing control schemes to achieve real-time DL-based myoelectric control of a bionic arm using hand gestures. We released all the source code at: https://***/MIC-Laboratory/EffiE

关键词： Deep learning Adaptation models Computational modeling Transfer learning Feature extraction real-time systems Electromyography

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Observability Analysis of Nonlinear Input-Linear systems Based on Lie Derivations Using Interval Arithmetic

Observability Analysis of Nonlinear Input-Linear Systems Bas...

引用

international conference on systems and Control (ICSC)

作者： Michelle Rosik Robert Dehnert Sabine Lerch Bernd Tibken School of Electrical Information and Media Engineering Automatic Control University of Wuppertal Wuppertal Germany

this paper presents a method to analyze the local observability of nonlinear systems with known inputs. For this purpose, an algorithm is presented that performs rank checking of the observability matrix using Lie derivatives. To overcome the limitations associated with inserting initial conditions and symbolically computing the derivatives, Interval arithmetic and Taylor series expansion with automatic differentiation are used. this allows observability to be analyzed over an entire domain. the algorithm provides the intervals for which observability cannot be proven by the rank criterion. this can be helpful when designing real-world applications. Two numerical systems are given as examples, with the first one having a constant input signal and the second one having an analytical input.

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Gesture to Speech: A Wearable Solution for Sign Language Translation

Gesture to Speech: A Wearable Solution for Sign Language Tra...

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international conference on Advanced computing and Communication systems (ICACCS)

作者： Balamanikandan A R Bindu Madhavi Pudi Abhiram Reddy Pinjari Ameer Sohel Pamala Vijay Kumar Ashokkumar N Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

this paper introduces a groundbreaking wearable device designed to address communication challenges faced by the global mute community. the device utilizes embedded flex sensors strategically placed on a glove to capture intricate sign language gestures. An embedded system, featuring a microcontroller (Arduino), processes these gestures through a sophisticated translation algorithm, converting them into both textual and speech output. the seamless integration of Bluetooth technology enables real-time wireless communication with external devices, such as smartphones or tablets. the device's versatile design ensures comfort and unobtrusiveness, making it suitable for various social, educational, and professional settings. Additionally, a rechargeable battery and efficient power management contribute to prolonged and sustainable usage. the embedded system's role is pivotal, acting as the cognitive hub that interprets and translates sign language gestures, fostering inclusivity and breaking down communication barriers. the translation algorithm, backed by machine learning techniques, enhances accuracy, making the device a reliable and practical solution. the optional inclusion of Text-to-Speech software further enriches the user experience, offering a comprehensive communication tool for the mute community. this paper discusses the device's technological architecture, design considerations, and real-world applications, emphasizing its transformative impact on global inclusivity. the research explores the potential for widespread adoption and its contribution to creating a more accessible and cohesive world.

关键词： Wireless communication Sign language Wireless sensor networks Bluetooth embedded systems real-time systems User experience Text to speech Wearable devices Speech processing

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An Approach to Reduce Network Effects in an Industrial Control and Edge computing Scenario 11

An Approach to Reduce Network Effects in an Industrial Contr...

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11th international conference on Cloud computing and Services Science (CLOSER)

作者： de Omena, Romulo A. L., V Santos, Danilo F. S. Perkusich, Angelo Univ Fed Campina Grande Embedded Syst & Pervas Comp Lab 882 Aprigio Veloso St BR-58429970 Campina Grande Paraiba Brazil

ISBN: (纸本)9789897585104

the cloud-based nature of Industry 4.0 enhances its flexibility and scalability features. To support time-sensitive and mission-critical applications, whereby low latency and fast response are essential requirements, usually cloud computing resources should be placed closer to the industry. the Edge computing concept combined with next-generation networks, such as 5G, may fulfill those requirements. this paper presents an experimental system setup that combines a Model Predictive Control approach with a compensation strategy to mitigate network delay and packet loss. the experimental system has two sides, namely, the edge and the local side. the former executes the controller and connects to the local side through a network. the latter is attached to the application and has lower computing capabilities. In our setup, the application under control is a two-wheeled mobile robot, which could act as an Automated Guided Vehicle. We defined two control objectives, the Point Stabilization, and the Trajectory Tracking, which ran through distinct network conditions, including delays and packet losses. these control objectives are only validation scenarios of the proposed approach but could be replaced by a real case path planner. the obtained results suggest that the approach is valid.

关键词： IIoT Industry 4.0 Edge computing 5G Model Predictive Control AGV

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