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Digital signal processing on 3-Axis Accelerometer

Digital Signal Processing on 3-Axis Accelerometer

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2023 international conference on intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Krishnakumar, Sreehari Vignesh Karthikeyan, R. Manoharan, V.K. Saritakumar, N. PSG College of Technology Department of ECE Coimbatore641658 India PSG Space Centre PSG College of Technology Coimbatore641658 India

ISBN: (纸本)9798350335835

This paper presents a method for processing 3-Axis accelerometer signals using a Cortex M4F microcontroller. The accelerometer is a device that measures acceleration, which is the rate of change of velocity. It is often used in applications such as motion sensing and gesture recognition. The process begins by acquiring the accelerometer signals and transforming them using the Fast Fourier Transform (FFT). The FFT is an algorithm that decomposes a signal into its frequency components, which can be useful for identifying patterns and trends in the data. The transformed signals are then passed through a digital filter, which is a mathematical tool that can be used to remove or enhance specific frequencies from a signal. In this case, the filter is used to eliminate or reduce certain frequencies that are not of interest. The purpose of this process is not specified, but it is likely that the accelerometer and microcontroller are being used to perform tasks such as analyzing device movements or identifying specific gestures made by a user. Overall, this paper provides an insight into the use of a 3-Axis accelerometer and Cortex M4F microcontroller for signal processing, with the application of the FFT and digital filter allowing for the extraction of useful information from the accelerometer signals that can potentially be used in a variety of applications. © 2023 ieee.

关键词： Fast Fourier transforms

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An intelligent System for Agricultural Process Management 9

An Intelligent System for Agricultural Process Management

引用

9th international conference on signal processing and intelligent systems, ICSPIS 2023

作者： Agusta, Yudi Julyantari, Ni Komang Sri Institut Teknologi Dan Bisnis Stikom Bali Informatics and Computer Faculty Denpasar Indonesia

ISBN: (纸本)9798350369922

Management of agricultural processes is an important part in ensuring the quality of agricultural products. Most of the management of agricultural processes is carried out with a manual management system where farmers in general already know the stages of agricultural activities and can always determine when a stage of the agricultural process must be carried out. This causes various obstacles, including several important processes that are often neglected due to the habits adopted by farmers. If the agricultural work is managed by multiple people, the agricultural stages may be carried out in an inaccurate pattern and time. Under these conditions, an intelligent system for agricultural process management needs to be built. The system provides various facilities including an automatic calculation facility for agricultural process time, a reminder system for the stages of the agricultural process as well as materials and supporting tools needed for each agricultural process. This research and development is carried out by reviewing unstructured information on the stages of agricultural processes and converting the information into a structured one. The resulting structured information includes stage names, objectives, risks involved, quality, implementation timeframe, costs, human resources, and other resources. For the purpose of determining the time/date of the implementation of the activity stages, other information such as conditions and the relativity of time to other activity stages are also reviewed. The system prototype has been analyzed and developed and is expected to be implemented and used to support better management of agricultural processes. © 2023 ieee.

关键词： intelligent systems

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Kinetic Electronics Based Soft Micro Robot with Surface Mounted Integration Circuit for Multiplex Control

Kinetic Electronics Based Soft Micro Robot with Surface Moun...

引用

2024 ieee international conference on robotics and Biomimetics, ROBIO 2024

作者： Matsunaga, Tatsuki Hayashi, Kenshi Sassa, Fumihiro School of Systems Life Sciences Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan Kyushu University Faculty of Information Science and Electrical Engineering 744 Motooka Nishi-ku Fukuoka819-0395 Japan

ISBN: (纸本)9781665481090

In this research, an electro thermal bimorph actuator with a shift register mounted in the drive unit was developed to simplify and downsize the control circuit of a soft robot. While conventional soft robots have flexible and adaptable characteristics, they require complex wiring and assembly processes, making large-scale mass production and miniaturization difficult. In this research utilized a kinetic electronics technology to develop a film robot that can control multiple actuators via parallel communication by incorporating signal processing integrated circuit (IC) chips. In the experiment, electro thermal bimorph actuator films were fabricated using polypropylene (OPP) and polyimide (PI) films, and a Texas Instruments surface-mount logic IC chip was attached to the film robot using conductive adhesive. In addition, electronic circuits were patterned by Au sputtering to control the actuators via shift registers. As a result of the experiment, it was confirmed that all actuators operated accurately through the shift register. This demonstrated that parallel signal conversion is possible through parallel communication using shift registers with multi-level connections using a small number of wires. This enables parallel signal conversion through multistage connections while reducing the number of wires connected to an external power supply, thereby simplifying and downsizing the control circuit of the soft robot. The results of this research will contribute to the design of next-generation soft robots, which are expected to realize more complex movements and advanced miniaturization. In the future, along with further expansion of the range of applications, practical use is expected in diverse fields, such as handling biological specimens and assembling micro-components. © 2024 ieee.

关键词： Surface mount technology

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Real-time pricing method through market auction based on multi-agent reinforcement learning 8

Real-time pricing method through market auction based on mul...

引用

8th international conference on Power and Renewable Energy, ICPRE 2023

作者： Hong, Peng He, Shuping Fang, Xiaohan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. The energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. The final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. The simulation results indicate that the proposed pricing method have a good performance. © 2023 ieee.

关键词： Market auction Multi-agent reinforcement learning Real-time pricing

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Air Purification robotics using Cloud and Deep Q Networks for Autonomous systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

引用

1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 ieee.

关键词： robotics

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Image processing-Based Methods to Improve the Robustness of Robotic Gripping 22

Image Processing-Based Methods to Improve the Robustness of ...

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ieee Joint 22nd international Symposium on Computational Intelligence and Informatics / 8th ieee international conference on Recent Achievements in Mechatronics, Automation, Computer Science and robotics (CINTI-MACRo)

作者： Takacs, Kristof Elek, Renata Nagyne Haidegger, Tamas Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary Obuda Univ Univ Res & Innovat Ctr EKIK Budapest Hungary Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

ISBN: (纸本)9798350398823

Image processing techniques are having a huge impact on most fields of robotics and industrial automation. Real-time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence-based image processing algorithms (relying usually on neural networks) are more common nowadays, "classical" image processing methods can also be used effectively for most modern applications. This paper focuses on optical flow-based image processing, proving its efficiency by presenting optical flow-based solutions for modern challenges in different fields of robotics, such as robot-assisted surgery and food processing. The application domain introduced in this paper is based on a smart robotic gripper designed to support automated robot cells in the meat industry. The gripper is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented real-time algorithm.

关键词： Image processing Optical flow Slip detection Robotic gripping

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Simplicial Vector Autoregressive Model For Streaming Edge Flows 48

Simplicial Vector Autoregressive Model For Streaming Edge Fl...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Krishnan, Joshin Money, Rohan Beferull-Lozano, Baltasar Isufi, Elvin Simula Metropolitan Center for Digital Engineering Sigipro Department Oslo Norway University of Agder Wisenet Center Department of Ict Grimstad Norway Delft University of Technology Intelligent Systems Department Delft Netherlands

ISBN: (纸本)9781728163277

Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network structures such as edges, triangles, etc. The proposed model shares parameters across the simplicial signals by leveraging the simplicial convolutional filter and captures structure-aware spatio-temporal dependencies of the time-varying processes. Targetting the streaming signals from the real-world nonstationary networks, we develop a group-lasso-based online strategy to learn the proposed model. Using traffic and water distribution networks, we demonstrate that the proposed model achieves competitive signal prediction accuracy with a significantly less number of parameters than the VAR models. © 2023 ieee.

关键词： Hodge Laplacians Simplicial convolution simplicial vector autoregressive model

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A toolbox for neuromorphic perception in robotics 8

A toolbox for neuromorphic perception in robotics

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8th international conference on Event-Based Control, Communication and signal processing (EBCCSP)

作者： Dupeyroux, Julien Stroobants, Stein de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab MAVLab Delft Netherlands Delft Univ Technol Fac Aerosp Engn Micro Air Vehicle Lab Delft Netherlands

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453493

The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the information, and interact with their environment. Research towards fulfilling the promises of high flexibility, energy efficiency, and robustness of neuromorphic systems is widely supported by software tools for simulating spiking neural networks, and hardware integration (neuromorphic processors). Yet, while efforts have been made on neuromorphic vision (event-based cameras), it is worth noting that most of the sensors available for robotics remain inherently incompatible with neuromorphic computing, where information is encoded into spikes. To facilitate the use of traditional sensors, we need to convert the output signals into streams of spikes, i.e., a series of events (+ 1,- 1) along with their corresponding timestamps. In this paper, we propose a review of the coding algorithms from a robotics perspective and further supported by a benchmark to assess their performance. We also introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.

关键词： Neuromorphic engineering Neural networks signal processing algorithms signal processing Robot sensing systems Sensor systems Robustness

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An Efficient Learning Algorithm for Phase Shift Optimization in RIS-Aided MISO Wireless systems 15

An Efficient Learning Algorithm for Phase Shift Optimization...

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15th ieee international conference on Wireless Communications and signal processing, WCSP 2023

作者： Tian, Jiawen Guan, Wenwen Li, Xiao Tsiftsis, Theodoros A. Liu, Hongwu Jinan University School of Intelligent Systems Science & Engineering Zhuhai China Southeast University National Mobile Communications Research Laboratory Nanjing China University of Thessaly Dept. of Informatics & Telecommunications Lamia Greece Shandong Jiaotong University School of Information Science & Electrical Engineering Jinan China

ISBN: (纸本)9798350324662

Due to dynamic change of the wireless environment, devising the local phase shift matrix for reconfigurable intelligent surface (RIS) can be a thought-provoking task. Therefore, in this paper, we come up with a deep reinforcement learning (DRL)-based beamforming optimization algorithm for RIS-aided multiple-input single-output communication systems. To be precise, we exploit a DRL-based structure based on twin-delayed-deep-deterministic-policy-gradient (TD3) algorithm to automatically adjust the phase shift of each unit at RIS to maximize the downlink received signal-to-noise ratio (SNR). Simulation results show that the raised TD3 algorithm performs a higher received SNR than the conventional DRL algorithm with a reduced running time. © 2023 ieee.

关键词： Deep reinforcement learning (DRL) passive phase shift design reconfigurable intelligent surface (RIS) TD3 algorithm

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Event-Based Adaptive intelligent Control for LSSs with Time-Varying Output Constraints 8

Event-Based Adaptive Intelligent Control for LSSs with Time-...

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8th ieee international conference on Vision, Image and signal processing, ICVISP 2024

作者： Zhang, Linchuang Cao, Liang Computer Science and Technology Bohai University Jinzhou 121013 China College of Mathematical Sciences Bohai University Jinzhou 121013 China

ISBN: (纸本)9798331541460

This paper explores a decentralized adaptive intelligent control problem for large-scale systems in nonstrict-feedback structure with external disturbances and time-varying output constraints. An effective event-triggered strategy is utilized to reduce the number of messages to be transmitted. Furthermore, an asymmetric time-varying barrier Lyapunov function is adopted to handle the problem of time-varying output constraints of nonlinear LSSs. Using Lyapunov stability theory, an adaptive neural network compensation controller with a first-order sliding-mode differentiator is developed to avoid the issues of unknown disturbances and 'explosion of complexity'. The developed control strategy assures that all signals are bounded. Finally, some simulation results indicate the validity of the developed control protocol. © 2024 ieee.

关键词： Adaptive neural control event-triggered mechanism large-scale systems time-varying output constraints

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