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International SoC Design Conference, ISOCC

作者： Chen-Hua Chen Guan-Yu Lai Chuan Lee Chang-Yu Wu Kuan-Lin Liu Shin-Chi Lai Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan Dept. of Automation Engineering National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

This study combines AI voice synthesis with IoT to create a real-time multilingual translation system. Front-end devices capture the speaker's voice, which is then played back in both original and translated forms, enabling consecutive interpreting. This system is ideal for multilingual scenarios. After digitization, audio signals undergo speech-to-text conversion, multilingual translation, and text-to-speech processing. Speech synthesis matches the speaker's voice characteristics, allowing listeners to hear the translation in the speaker's voice. Both voice-to-text conversion and translated audio playback use Wi-Fi for data transmission, facilitating future applications. In validation, the average translation and transmission time for each speech segment were 410 ms and 3.38 s, respectively. The synthesized voice similarity analysis achieved an accuracy of 88%, showcasing the method's potential for system integration and development.

关键词： Accuracy System integration Real-time systems Text to speech Data communication Artificial intelligence Wireless fidelity Speech to text

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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Fast Measurement of Impedance Calculation for Electrochemical Impedance Spectroscopy

Fast Measurement of Impedance Calculation for Electrochemica...

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International SoC Design Conference, ISOCC

作者： En-Chi Yang Suz-Ting Wang Kusn-Lin Liu Wen-Ho Juang Ming-Hwa Sheu How-Chiun Wu Shin-Chi Lai Dept. Electronics Engineering / Dept. Doctor’s Program of Smart Industry Technology Research and Design National Yunlin University of Science and Technology / National Formosa University Douliu Taiwan / Huwei Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Natural Biotechnology Nanhua University Chiayi Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This paper presents a design for a fast measurement electrochemical impedance spectroscopy (EIS) system, which differs from the traditional single-frequency point calculation approach. The proposed system is capable of simultaneously measuring 23 frequency points within the frequency range of 1Hz-10KHz. Additionally, it has an impedance measurement range of 100$\omega$ - 1M$\omega$. The experimental results demonstrate that the average MRE and PAE are both below 0.4% and 1°, with conditions of R=9940 $\omega$ and C=1.02uF.

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Convolutional Neural Network-based Keyword Classification for Mixer Control

Convolutional Neural Network-based Keyword Classification fo...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Suz-Ting Wang Jeng-Dao Lee Wen-Ho Juang Ming-Hwa Sheu Shin-Chi Lai Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Dept. Doctor’s Program of Smart Industry Technology Research and Design National Formosa University Huwei Taiwan Dept. Doctor’s Program of Smart Industry National Yunlin University of Science and Technology Douliu Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

This study proposed a keyword recognition of microphone feed-in, achieving the effect of allowing users to control the real-time mixer system intelligently. The proposed system structure includes a front-end circuit to capture voice and Mel-scale Frequency Cepstral Coefficients (MFCC) features in conjunction with a Convolutional Neural Network (CNN) algorithm. The testing results show that the lightweight CNN model achieves a keyword recognition accuracy rate of 96.4%.

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： John P. Valcourt Franya M. Chandler Chamma Avrelus Jou-Yi Lee Ali Ihsan Gullu Syed H. Shah Dept. of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in ECE Yuan Ze University Taoyuan Taiwan Mechanicai/Industrial Engineering DE Yuan Ze University Taoyuan Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Defense Electronics Engineering Gebze Technical University Kocaeli Turkiye

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further.

关键词： Q-learning Accuracy Navigation Friction Object detection Robot sensing systems Multitasking

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Modified Turnstile Antenna Design for Multi-Band and Broadband Applications

Modified Turnstile Antenna Design for Multi-Band and Broadba...

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers Dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

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An Enhanced System for Wrong-Way Driving Vehicle Detection with Road Boundary Detection Algorithm

An Enhanced System for Wrong-Way Driving Vehicle Detection w...

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2022 International Conference on Industry sciences and computer science Innovation, iSCSi 2022

作者： Suttiponpisarn, Pintusorn Charnsripinyo, Chalermpol Usanavasin, Sasiporn Nakahara, Hiro TAIST Tokyo Tech ICTES Program Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Dept. of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. © 2022 Elsevier B.V.. All rights reserved.

关键词： Hough transforms

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Edge-Based Infrared-Ultrasonic Anti-Collision Radar System for Robotic Navigation: *Applications of Cost-effective Bisensory System for Obstacle Detection, Tracking, and Avoidance

Edge-Based Infrared-Ultrasonic Anti-Collision Radar System f...

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IEEE Conference on Technologies for Homeland Security

作者： Kosisochukwu Pal Nnoli Mbadiwe Samuel Benyeogor Jones Ifeanyi Bolu Oladayo Olufemi Olakanmi Dept. of Computer Science and Electrical Engineering Jacobs University Bremen Germany Dept. of Physics Westfälische Wilhelms-Universität Münster Münster Germany Junior Information Technology Analyst Program NPower Canada Toronto Canada Dept. of Electrical and Electronics Engineering University of Ibadan Ibadan Nigeria

ISBN: (纸本)9781665494052

Significant progress has been made in the development of an edge computing system for the fusion, representation, and visualization of dual-sensor data for obstacle avoidance control of mobile robots. This involves the use of an error-filtering covariance and averaging algorithm to logically fuse distance measurements from a pair of infrared and ultrasonic sensors, which was instrumented into a robot and used to generate radar visuals to track the proximity of ambient obstacles within 180 degrees spanning ahead of the robot. Hands-on experiments were performed to evaluate the performance and applicability of the system in real-time. The results show that the developed system is viable and robust. In line with the emerging field of edge computing, this work is an efficient, portable, and cost-effective approach to developing mobile robotic systems.

关键词： Visualization Navigation Fuses Sensor fusion Robot sensing systems Radar tracking Acoustics

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Detection of Wrong Direction Vehicles on Two-Way Traffic 13

Detection of Wrong Direction Vehicles on Two-Way Traffic

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13th International Conference Knowledge and Systems Engineering, KSE 2021

作者： Suttiponpisarn, Pintusorn Charnsripinyo, Chalermpol Usanavasin, Sasiporn Nakahara, Hiro Thammasat University Ictes Program Taist Tokyo Tech Sirindhorn International Institute of Technology Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand Thammasat University School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Pathum Thani Thailand Tokyo Institute of Technology Dept. of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665499750

In 2018, around 22,000 people were killed in road accidents in Thailand and more than 80% of road accidents involved motorcycles. There are several reasons and one of them is motorcycles driving in the wrong direction. Our paper proposes a system for road surveillance to detect and track moving vehicle's direction on two-way traffic roads from CCTV viewpoint using deep learning where the roadside noise is filtered out. The system can determine whether the vehicles drive in the correct direction in their lane. In this case, we will focus on motorcycles. To set up the system, there are two main steps: draw the area of interest and validate the correct direction. In the direction validation process, we detect and track vehicles by using YOLOv4-tiny and Deep SORT tracking algorithm. The system returns a satisfactory and accurate result for detecting direction without noise, such as moving objects on the sidewalk or moving vehicles in an unwanted area. © 2021 IEEE.

关键词： Motorcycles

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Scalable and efficient neural speech coding: A hybrid design

arXiv

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arXiv 2021年

作者： Zhen, Kai Sung, Jongmo Lee, Mi Suk Beack, Seungkwon Kim, Minje Department of Computer Science and Cognitive Science Program Indiana University BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Dept. of Intelligent Systems Engineering Indiana University United States

We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine. The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process. We achieve efficiency by introducing compact model components to NWC, such as gated residual networks and dept.wise separable convolution. Furthermore, the proposed models are with a scalable architecture, cross-module residual learning (CMRL), to cover a wide range of bitrates. To this end, we employ the residual coding concept to concatenate multiple NWC autoencoding modules, where each NWC module performs residual coding to restore any reconstruction loss that its preceding modules have created. CMRL can scale down to cover lower bitrates as well, for which it employs linear predictive coding (LPC) module as its first autoencoder. The hybrid design integrates LPC and NWC by redefining LPC’s quantization as a differentiable process, making the system training an end-to-end manner. The decoder of proposed system is with either one NWC (0.12 million parameters) in low to medium bitrate ranges (12 to 20 kbps) or two NWCs in the high bitrate (32 kbps). Although the decoding complexity is not yet as low as that of conventional speech codecs, it is significantly reduced from that of other neural speech coders, such as a WaveNet-based vocoder. For wide-band speech coding quality, our system yields comparable or superior performance to AMR-WB and Opus on TIMIT test utterances at low and medium bitrates. The proposed system can scale up to higher bitrates to achieve near transparent performance. © 2021, CC BY.

关键词： Convolution

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