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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices 48

Benchmarking Convolutional Neural Network Inference on Low-P...

引用

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

作者： Ferraz, Oscar Araujo, Helder Silva, Vitor Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Institute of Systems and Robotics Portugal

ISBN: (纸本)9781728163277

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s. © 2023 ieee.

关键词： Artificial Intelligence Benchmarking Edge devices GPUs Inference Engine IoT Low-power

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Research on current transformer signal recognition method based on convolutional network 6

Research on current transformer signal recognition method ba...

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6th ieee international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Lin, Jianbang Deng, Zijian Cao, Junwei Chen, Yuxuan Zhang, Yang Yangjiang Power Supply Bureau of Guangdong PowerGrid Limited Liability Company Yangjiang Guangdong China

ISBN: (纸本)9798350389418

With the rapid development of smart grid and industrial automation, the accuracy and reliability of current transformers as key components in the power system have become increasingly important. This article proposes a current transformer signal recognition method based on convolutional neural networks, aiming to improve the efficiency and accuracy of distorted signal recognition. By systematically studying the key components of neural networks such as convolutional layers, pooling layers, Flatten layers, fully connected layers, BN layers, and Dropout layers, an efficient signal feature extraction method has been designed. Additionally, a distortion signal model for current transformers has been constructed, and a denoising solution has been proposed to reduce interference factors during the signal processing. The experimental section validates the effectiveness and stability of the proposed method in identifying distorted signals of current transformers through a series of tests in a simulated environment. The results demonstrate that the new method achieves high recognition accuracy in various complex scenarios, providing technical support for the intelligent operation and maintenance of power systems. © 2024 ieee.

关键词： Distribution transformers

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Leveraging Support Vector Machines for Precise Attenuation Prediction in WDM-FSO systems

Leveraging Support Vector Machines for Precise Attenuation P...

引用

2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Kebaili, Rima Driz, Samia Fassi, Benattou University of Djillali Liabes Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering Sidi Bel Abbes22000 Algeria

ISBN: (纸本)9798331527396

Free-Space Optical (FSO) communication systems offer high bandwidth and interference immunity, making them attractive for next-generation networks. However atmospheric conditions significantly impact the performance of FSO systems, where attenuation is significant challenge. This research investigates the application of Support Vector Machines (SVM), a powerful machine learning technique, to accurately predict attenuation in Wavelength Division Multiplexing (WDM) FSO systems. By leveraging OptiSystem simulations to generate extensive datasets, we train and evaluate SVM models to predict attenuation levels based on meteorological parameters. The models' performance is assessed using ROC curves and confusion matrices to measure their ability to distinguish between high and low attenuation scenarios. The findings of this research provide valuable insights into the effectiveness of SVM for predicting attenuation in WDM-FSO systems, enabling proactive mitigation strategies and enhancing system reliability. © 2024 ieee.

关键词： Support vector machines

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Lightmyography Based Decoding of Human Intention Using Temporal Multi-Channel Transformers

Lightmyography Based Decoding of Human Intention Using Tempo...

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ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Godoy, Ricardo V. Dwivedi, Anany Shahmohammadi, Mojtaba Liarokapis, Minas Univ Auckland Dept Mech & Mechatron Engn New Dexter Res Grp Auckland New Zealand Friedrich Alexander Univ Erlangen Nurnberg Inst Autonomous Syst & Mechatron Erlangen Germany

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

For the development of muscle-machine interfaces (MuMIs), researchers have relied mainly on Electromyography (EMG) signals. However, these signals require complex hardware systems, as well as specialized signal processing and feature extraction methods. To overcome these issues, in our previous work, we proposed a novel MuMI for decoding human intention and motion, called Lightmyography (LMG). To improve the performance of this interface even further, in this work, we employ two novel deep learning techniques called Temporal Multi-Channel Transformer (TMC-T) and Temporal Multi-Channel Vision Transformer (TMC-ViT) for the classification of hand gestures based on the LMG data. The performance of these two Transformer-based methods is evaluated and compared with other well-known deep learning and classical machine learning methods. This work also addresses the influence of varying parameters defined during the training phase of decoding models, such as the size and shape of the input data packet. A series of data augmentation techniques were also employed to generate synthetic data and increase the dataset size so as to train deep learning models more efficiently.

关键词： Deep learning Training Adaptation models signal processing algorithms signal processing Transformers Data models

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Kaiser Self-convolution Window Harmonic Analysis Algorithm for Distributed Photovoltaic Power Generation signal processing 4

Kaiser Self-convolution Window Harmonic Analysis Algorithm f...

引用

4th international conference on intelligent Power and systems, ICIPS 2024

作者： Qiu, Xinyu Hu, Kangmin Diao, Yinglong Long, Zhaozhi Li, Wenting Fan, Jiawei Wang, Zhehao State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Wuhan Branch China Electric Power Research Institute Wuhan China Wuhan Branch China Electric Power Research Institute Yichang China

ISBN: (纸本)9798331532765

The spectrum is complex and the interference is serious when there are non-integer inter-harmonics. Aiming at this, a fourth-order Kaiser self-convolution window function obtained by convolution operation is proposed. Because of its characteristics, it can improve frequency resolution and reduce spectrum interference. At the same time, the spectral phase difference correction method is used to reduce the fence effect and reduce the amount of calculation. The simulation example analysis shows that the algorithm can accurately measure the harmonic and interharmonic parameters, has high measurement accuracy, and has certain anti-frequency interference. © 2024 ieee.

关键词： Harmonic analysis

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Adaptive Broad Learning Network for LPI Radar Waveform Recognition 9

Adaptive Broad Learning Network for LPI Radar Waveform Recog...

引用

9th international conference on intelligent Computing and signal processing, ICSP 2024

作者： Wang, Dong Cui, Tianshu Li, Ruike Huang, Yonghui Zhu, Yan National Space Science Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China China Academy of Aerospace Science and Innovation Beijing China Key Laboratory of Electronics and Information Technology for Space Systems Beijing China

ISBN: (纸本)9798350376548

Low probability of intercept (LPI) radar waveform recognition is a crucial branch in the field of electronic reconnaissance, serving as a vital method for acquiring information from non-cooperative radar sources. In recent times, deep learning models have demonstrated significant breakthroughs in the field of LPI radar waveform recognition (LWR). While these models significantly enhance recognition accuracy, the abundance of parameters and computations in the model renders training time-consuming, and the model structure lacks flexibility and scalability. To address these challenges, we propose an adaptive broad learning network (ABLN) for LWR. The ABLN transforms the time-frequency images (TFIs) of radar signals into feature nodes and enhancement nodes, with waveform types represented as output nodes. We further introduced a strategy for adaptively adjusting network parameters to reduce computational overheads, facilitating expedited optimization of the model structure. The simulation results show that ABLN has superior performance compared with other LWR methods. © 2024 ieee.

关键词： Image enhancement

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Eagle Strategy based Improved Adaptive Control System for an Inverted Pendulum - Mobile Robot 3

Eagle Strategy based Improved Adaptive Control System for an...

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3rd ieee international conference on intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Jarin, T. Deepanraj, B. Beula, Annie Steffy Vignesh, K. Ezhil Jyothi Engineering College Department of Eee Kerala Thrissur India Prince Mohammad Bin Fahd University Department of Mechanical Engineering Al-Khobar Saudi Arabia Stella Mary's College of Engineering Department of Eee Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798350361186

Navigation robot is required in industries to perform the assigned task. Therefore the robot should choose the shortest path to reach the given target. The computation time and cost should be less while making the decision. This work explains the position balancing of the Two Wheel Inverted Pendulum-Mobile Robot (TWIP-MR) system using the nature-inspired algorithm Eagle Strategy with Particle Swarm Optimization (ESPSO). According to stability concerns, this is an unstable open loop system exhibiting nonlinear behavior. The design of the system focuses on the balanced movement of the robot while moving towards the intended position. Two loops are offered while designing TWIP-MR, one for balancing the linear displacement and the other for maintaining the appropriate angular movement. This research uses a mathematical technique to provide an analysis of the TWIP-MR system. The random global search, intensive local search and a levy walk is adopted from Eagle search strategy. Particle Swarm Optimization is included in the local search stage of the ES. The PID controller is used in order to reduce the positional errors. © 2024 ieee.

关键词： Adaptive control systems

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Design and signal Quality Assessment of a Novel Low-Cost Wireless EMG Sensor 14

Design and Signal Quality Assessment of a Novel Low-Cost Wir...

引用

14th ieee international conference on Cyber Technology in Automation, Control, and intelligent systems, CYBER 2024

作者： Ling, Ziqin Chen, Jiangcheng Li, Yaonan Xi, Ning University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong Shenzhen Academy of Robotics Shenzhen China

ISBN: (纸本)9798331506056

Electromyography (EMG) sensors have been widely used for assessing muscle activation state and human-robot interaction (HRI). However, available commercial EMG sensors are expensive and inconvenient for real-time applications. The aim of this study is to design and validate a novel low-cost wireless EMG sensor. Specifically, the wireless EMG sensor is equipped with an analog-front-end (AFE), radio frequency (RF) module, and controller area network (CAN) bus, which achieves wireless data transmission and provides an interface for real-time HRI. The evaluation of the designed system was done by a series of exercises performed by volunteers. Raw signal., mean absolute value (MAV), zero crossings (ZC), slope sign changes (SSC), waveform length (WL), and median frequency (MDF) are used for comparing wireless EMG sensors and commercial sensors. In addition, Spearman's correlation (SC), and intra-class cross-correlation (ICC) are used as two validation indicators. The result shows that the SC and ICC between the raw signals of the two types of sensors are 0.6156 and 0.7780, respectively. The SC of MAV, ZC, SSC, WL, and MDF are 0.9708, 0.6740, 0.8023, 0.9663, and 0.9476, respectively. The ICC of MAV, ZC, SSC, WL, and MDF are 0.9670, 0.7340, 0.7910, 0.9540., and 0.8230, respectively. The results indicate a good agreement between the two systems and confirm the reliability of the wireless EMG sensor's design. The wireless EMG sensor can be further applied to real-time HRI and muscle fatigue assessment. © 2024 ieee.

关键词： Human robot interaction

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Total Harmonic Distortion Reduction in a Power Grid: A UPQC Based Approach 3

Total Harmonic Distortion Reduction in a Power Grid: A UPQC ...

引用

3rd international conference on robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Pathik, Bishwajit Banik Islam, Sanjid Akram, Nur Mohammad Zaman, Md. Salman Al-Nahian, Md. Abdullah American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350346435

This study shows a novel design for a unified power quality conditioner (UPQC) to improve the power quality. The designed UPQC has two active power filters (APF) in shunt and series combinations. PWM (pulse width modulation) and hysteresis controls are the two control mechanisms used in this study. To reduce harmonics, a direct-quadrature zero (dq0) conversion is performed. In case 1, for a single-bus system, the total harmonic distortion (THD) of the source current in the system without UPQC was 22.22%. After applying the designed method, THD has been reduced significantly to 2.68%. In case 2, for multi-bus systems, the THD was 20.62% and 5.05% with and without the UPQC, respectively. Here the THD in the grid input voltage was 20.62%, whereas the output voltage was 0.80%. The ieee 519-2014 standard for voltage and current harmonics distortion criterion for electrical system design is followed in the research. © 2023 ieee.

关键词： Harmonic distortion

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Design and VLSI Implementation of Hybrid Analog to Digital Converter for High Frequency Applications

Design and VLSI Implementation of Hybrid Analog to Digital C...

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2023 ieee international conference on Distributed Computing, VLSI, Electrical Circuits and robotics, DISCOVER 2023

作者： Kumar, Keerthan Bhatt, Punyashree Shetty, Prathap S Chandra, Vinod Kumar, Ashwin Sahyadri College of Engineering and Management Department of Electronics and Communication Engineering Mangaluru India

ISBN: (纸本)9798350340129

The users in a wire-free era are benefiting from the advancement of wireless technologies. The transmitter and receiver of the wireless communication systems use digital signal processing. In nature all the signals are analog. In view of the analog signal's processing complexity, it is transformed to digital form to facilitate processing. Because of advancements in integrated circuit technology during the previous decade, expansion in the digital signal processor industry has been rapid. The ever-increasing speed of wireless communication networks places enormous demands on the performance of high-speed low-resolution analog-to-digital converters. Analogue signals are complicated to process, so they are transformed to digital form to make processing simpler. Over the past ten years, advancements in integrated circuit technology have accelerated the development of digital signal processors. As a result, an interface between systems for processing digital and analogue signals is provided by analog-to-digital converters. Good analog-to-digital converters must meet extremely high demands due to the wireless communication systems' ongoing advancement in speed. This research deals with modified 3-bit Flash Analog- to- Digital Converter (ADC). This report's converter is built using a two-step flash approach. An efficient method for creating analog-to-digital converters is the two-step flash process. The converter's sampling frequency is around 2 MHz, and its integral and differential nonlinearities are both less than 1 LSB with a power dissipation of under 40 mW. By creating a software reference model with MATLAB Simulink, the pre design and verification of the redesigned flash ADC were completed. The updated flash converter sub-blocks' CMOS level schematic diagram was recorded and created in Cadence Virtuoso utilizing 180nm technology and an operational voltage of 1.8 volts. Cadence has verified the designed ADC. © 2023 ieee.

关键词： Digital signal processors

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