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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Design Of A ‘ Drive Wave ‘ System based on Communications System Remote Real-time Running Text and Speech to Text Converter using Light Emitting Diode (LED) Display

Design Of A ‘ Drive Wave ‘ System based on Communications ...

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computer sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Juan Felix Christopher Sandy Setiawan Elioenai Sitepu Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Drive Wave is a device that can help drivers communicate with other road users by displaying familiar words and icons as well as a speech to text feature that can make it easier for drivers to communicate with other road users. in this work, a system design prototype consisting of two devices, namely Remote and LED message board. The most spoken words used and icons displayed on the LED message board are controlled wireless using a Remote using the ESP-NOW protocol and Bluetooth Low Energy (Bluetooth LE). The two ways of he LED message board controlled by the remote are manual and automatic speech to text converter system controlled the LED message board displayed. the manual way, the user pressed the button to choice the word to be displayed. in the automatic voiced control, the LED displayed the word uttered by the user. the automatic voiced controlled applied Mel-frequency cepstral coefficients (MFCC) as feature extraction techniques and Long Short-Term Memory (LSTM) method for word identification methods. The results showed that the automatic voice controlled 99.78% and 100% for training and validating accuracy, respectively. the real testing result showed that 24 times testing which have 100% accuracy.

关键词： Meters Pediatrics Roads Drives Light emitting diodes Mel frequency cepstral coefficient Long short term memory Speech to text Vehicles Testing

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging 23

Improvement of Depth Mapping and Occlusion Removal Method Us...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Sakanoue, Haruka Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving. © 2023 ICROS.

关键词： Mapping

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

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42nd Chinese Control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy 23

A Study on Coherence Length and Interferometry in Digital Ho...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Koga, Takahiro Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object. © 2023 ICROS.

关键词： Computational Imaging

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A Novel Low Light Image Enhancement Method Based on Multi-Attention Generative Adversarial Networks 10

A Novel Low Light Image Enhancement Method Based on Multi-At...

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Zhang, Saijun Hou, Xu School of Computer Science and Engineering Northeastern University Shenyang China Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798350332933

Images taken in low light realistic conditions cannot capture enough illumination to show more details, so that the recovered images show color deviation and unclear edge textures along with much noise information in the experiments of low light image enhancement. In order to obtain high brightness and clear images, this paper designs a network based on multi-attention Generative Adversarial Networks for low light image enhancement. The network uses a low illumination enhancement module that incorporates the non-local illumination attention and edge aware attention proposed in this paper. Non-local illumination attention can combine global information to quickly capture the relationship of long-range features, solving the color deviation problem while reducing the number of convolution layers. Edge aware attention focuses on local information at the edges, highlighting the edge contours of the image while smoothing out the noise. These two attentions serve as a complement to each other. And Attentional Feature Fusion is used to fuse the features of different branches and retain more valid information. It is proved that our proposed low light enhancement network is superior to other networks in terms of color fidelity, edge clarity, and denoising. © 2022 IEEE.

关键词： Image enhancement

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Soft Cap for Eversion Robots

arXiv

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arXiv 2023年

作者： Suulker, Cem Skach, Sophie Kaleel, Danyaal Abrar, Taqi Murtaza, Zain Suulker, Dilara Althoefer, Kaspar Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science School of Electronic Engineering and Computer Science United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Growing robots based on the eversion principle are known for their ability to extend rapidly, from within, along their longitudinal axis, and, in doing so, reach deep into hitherto inaccessible, remote spaces. Despite many advantages, eversion robots also present significant challenges, one of which is maintaining sensory payload at the tip without restricting the eversion process. A variety of tip mechanisms have been proposed by the robotics community, among them rounded caps of relatively complex construction that are not always compatible with functional hardware, such as sensors or navigation pouches, integrated with the main eversion structure. Moreover, many tip designs incorporate rigid materials, reducing the robot’s flexibility and consequent ability to navigate through narrow openings. Here, we address these shortcomings and propose a design to overcome them: a soft, entirely fabric based, cylindrical cap that can easily be slipped onto the tip of eversion robots. Having created a series of caps of different sizes and materials, an experimental study was conducted to evaluate our new design in terms of four key aspects: eversion robot made from multiple layers of everting material, solid objects protruding from the eversion robot, squeezability, and navigability. In all scenarios, we can show that our soft, flexible cap is robust in its ability to maintain its position and is capable of transporting payloads such as a camera across long distances. We also demonstrate that the robot’s ability to move through restricted aperture openings and indeed its overall flexibility is virtually unhindered by the addition of our cap. The paper discusses the advantages of this design and gives further recommendations in relation to aspects of its engineering. © 2023, CC BY.

关键词： Robots

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Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN 23

Noise Removal in Scattering Media Enviorment Using Peploarap...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Numata, Riki Kim, Hyun-Woo Ono, Seiya Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed. © 2023 ICROS.

关键词： Smoke

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Topic Relation Graph Construction to Identify Challenges in Introducing programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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