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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Wuyoung Jang Sangho Lee Jinhoon Jo Jueun Jung Donghyeon Han Kyuho Lee Department of Electrical Engineering Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge MA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

A super resolution (SR) accelerator with variable bit compression method is proposed for 4K restoration with > 60 frames-per-second (fps) in mobile devices. Since 4K SR has huge intermediate feature maps, large external memory access (EMA) bandwidth is required that recent mobile processors cannot support. Previous SR processors quantized feature maps and weights for EMA reduction, but it has a trade-off between data compression rate and performance drop. To facilitate > 60 fps 4K SR on mobile processor, this work proposes two key features: 1) Tile-based distribution-aware statistical encoding that results in 61.2% compression rate without information loss; 2) An energy-efficient SR processor which supports variable bit encoding, achieving 59.1% reduction of EMA. Designed with 28 nm CMOS technology, the proposed system can accelerate ×2 scale 4K image restoration at 68.7 fps. It shows 3.69 TOPS of peak performance and 422.1 Mpixels/J of energy efficiency, achieving 1.35× higher energy efficiency than the previous SR processor.

关键词： Performance evaluation program processors Image coding Superresolution Energy efficiency Encoding Mobile handsets

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Simulation of Photonic Metasurfaces using a Surface Integral Equation Solver with Generalized Sheet Transition Conditions

Simulation of Photonic Metasurfaces using a Surface Integral...

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Antennas and Propagation (LACAP), IEEE Latin American Conference on

作者： Sebastian Celis Sierra Partha Mondal Ran Zhao Hakan Bagci Electrical and Computer Engineering (ECE) Program King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331527587

ISBN: (纸本)9798331527594

Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. GSTCs connect the electromagnetic fields on both sides of the metasurface using equivalent susceptibility tensors and avoid modelling of geometrically intricate metasurface unit cells by the SIE solver. Susceptibility tensors are obtained from the transmission and the reflection coefficients associated with a unit cell. A numerical example, involving a metasurface that supports high-quality factor resonances and excited by a Gaussian beam, is presented to demonstrate the accuracy of the proposed approach.

关键词： Tensors Accuracy Integral equations Metasurfaces Mathematical models Numerical models Reflection coefficient Electromagnetic fields Dispersion Photonics

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm 2

Impact of Image Enhancement for Osteoporosis Detection Based...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Iman, Riza Dianil Roslidar, Roslidar Muchtar, Kahlil Amalia, Amalia Syaryadhi, Mohammad Mufti, Alfatirta Maulina, Novi Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an F1-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance. © 2023 IEEE.

关键词： Image enhancement

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Com...

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IEEE Conference on Vehicular Technology (VTC)

作者： Aamer Mohamed Huroon Yu-Chih Huang Li-Chun Wang Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Institute of Communications Engineering National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

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An Empirical Generation Technique on Background Music Using Gated Recurrent Neural Networks

An Empirical Generation Technique on Background Music Using ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Wei-Ho Tsai International Graduate Program of Electrical Engineering and Computer Science (IEECS) National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, this BGM is necessary to enhance the intended message expressed to the other audience. This work aimed to provide the model network of GRU which is based on RNN to generate multi-label genres of music by using the open source of GTZAN to evaluate the new BGM. Our GRU networks can solve the vanishing gradient problem by utilizing both the reset gate and the update gate on the network. In the results, we achieved a new BGM that synchronized with the human mood which made more variety of sounds.

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Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson's Disease

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IEEE Transactions on Computational Social Systems 2023年第4期10卷 1607-1621页

作者： Zhang, Chao Ding, Juanjuan Zhan, Jianming Sangaiah, Arun Kumar Li, Deyu Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University School of Computer and Information Technology Taiyuan030006 China Hubei Minzu University School of Mathematics and Statistics Enshi445000 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson's disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions

关键词： Diagnosis

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A Comparison of Keras Application Models with Pre-Trained Weights in Predicting the Disease of Lanzones Leaf 14

A Comparison of Keras Application Models with Pre-Trained We...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Muhali, Adzmer Linsangan, Noel Graduate School Mapua University Intramuros Manila Philippines Department of Computer Science Information Technology and Engineering Sulu State College Sulu Jolo Philippines School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (纸本)9781665464932

Much research has already been done to detect diseases on plant leaves using different architectures of the Tensorflow Keras Application. Despite this, no existing or ongoing research on the disease on Lanzone's leaf has been conducted. Lanzones also provide essential health benefits to people and the market demands. This study helps researchers choose the Keras Application's exemplary architecture for detecting the disease on Lanzones Leaf. Hence this study compares four (4) architectures of Keras Application that provide impressive results in other plant disease classification such as VGG16, VGG19, ResNet101, and ResNet152V2 to determine the best model for detecting the Lanzones Leaf disease. In this study, the models saw the healthy leaf and diseases such as Algal Leaf Spot and Leaf Blight Spot of Lanzones Tree. The methods used pre-processing and augmentation on the collected dataset, as well as training the models for more epochs, to improve the models' performance and learning process. The analysis and evaluation results show that VGG16 is the best model among those models utilized in detecting diseases of lanzones leaves;hence, the VGG16 achieved 89.09% of overall accuracy. © 2022 IEEE.

关键词： Plants (botany)

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Daily Peak Load Forecast of Banda Aceh City Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Daily Peak Load Forecast of Banda Aceh City Using Adaptive N...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Heldi Tindra Ira Devi Sara Ramzi Adriman Graduate program of Electrical Engineering Universitas Syiah Kuala Banda Aceh Aceh Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Aceh

electrical energy has become a fundamental need for society to achieve economic and technical efficiency. To meet the demand for electrical energy, the thing that is done is Electric Load Forecast. In this study, we developed a daily peak load forecast model for Banda Aceh City by considering data on temperature, humidity, and today’s electricity load data at peak hours. Forecasts are made using artificial intelligence, namely, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. Software used Matlab R2015a to create a daily peak load forecast model based on the neuro-fuzzy designer toolbox. The ANFIS model developed is a variation of triangular, trapezium, and Gaussian membership function types, with each membership function equipped with 3 and 4 variable fuzzy sets. This study uses the MAPE instrument to measure the accuracy of the developed ANFIS model. The results obtained through simulations that have been carried out, all ANFIS Models produce MAPE values below 10%. This indicates that the developed ANFIS Model is very appropriate to be used for Daily Peak Load Forecast in Banda Aceh.

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3D LiDAR-Based Lane Line Recognition and Obstacle Avoidance for Autonomous Mobile Robot - A Robust Approach without Image Sensors

3D LiDAR-Based Lane Line Recognition and Obstacle Avoidance ...

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SICE Annual Conference

作者： Takuya Yamakawa Koki Kubota Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester MI USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

The Intelligent Ground Vehicle Competition (IGVC) is an annual competition for autonomous mobile robots held in the U.S. One of the vital functions of success in this competition is the autonomous lane-following function, which requires stable lane line recognition while avoiding obstacles and staying within lanes. The most popular approach for lane line recognition employs image sensors such as cameras. However, this approach has inherent weaknesses due to changes in outdoor lighting conditions, which are caused by limitations in the dynamic range of the image sensor. To overcome the weaknesses of image sensor-based lane line recognition, we propose a new method that utilizes the reflection intensity of the point cloud acquired by 3D LiDAR. The combination of point clouds and intensity enables the detection of both lane lines and obstacles without the effect of lighting condition changes. In this paper, we propose a novel, safer, and more robust lane-following algorithm that uses only 3D LiDAR, without the need for an image sensor. Lane line detection is performed by extracting a group of points with reflection intensities within a certain range while estimating the direction of the lane line and using a reflection intensity threshold value. Detected lane lines and obstacles are represented as a one-dimensional evaluation cost function, and a local target destination is determined in the direction with the lowest cost. This enables robust control regardless of outdoor lighting conditions. The performance of lane-following, obstacle avoidance, and real-time operation were evaluated in a simulation environment.

关键词： Point cloud compression Image sensors Three-dimensional displays Laser radar Image recognition Velocity control Lighting Reflection Real-time systems Collision avoidance

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Classification of Lanzones Tree Leaf Diseases Using Image Processing Technology and a Convolutional Neural Network (CNN) 4

Classification of Lanzones Tree Leaf Diseases Using Image Pr...

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4th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2022

作者： Muhali, Adzmer S. Linsangan, Noel B. Graduate School Mapua University Intramuros Manila Philippines Information Technology and Engineering Sulu State College Department of Computer Science Sulu Jolo Philippines School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Nowadays, many artificial intelligence systems are developed to detect and classify plant leaf diseases via image classification, and those researches achieved high-performance results. However, those Artificial Intelligence Systems categorize various leaf diseases, but none precisely for Lanzones. Briefly, no existing or current research has been studied to detect and classify leaf diseases of Lanzones tree in the early stages using an Artificial Intelligence System. This paper presents a device for categorizing the healthy and with diseases (Algal Leaf Spot and Leaf Blight Spot) on the leaves of the Lanzones tree thru image classification using deep learning. It is limited to the device's development and implementation in the target locale. The methods applied include data pre-processing and augmentation techniques to the created CNN model. This will be integrated into a system using the Raspberry Pi 4 Model B and the Tensorflow Lite Interpreter. The performance analysis and evaluation of the system were based on the confusion matrix, which shows how the system successfully classified the healthy and disease of Lanzones leaves with an accuracy rate of 80%. © 2022 IEEE.

关键词： Neural network models

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