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Solution Processable Mos2 Based Memristive Synapse for Brain Inspired Computing

Solution Processable Mos2 Based Memristive Synapse for Brain...

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IEEE Conference on Nanotechnology

作者： Hanrui Li Dayanand Kumar Nazek El-Atab Smart Advanced Memory Devices and Applications (SAMA) labs Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering (CEMSE) Division KAUST Thuwal Saudi Arabia

As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.

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A Discontinuous Galerkin Time-Domain Scheme to Model Lasing Dynamics in Four-Level Two-Electron Atomic Systems

A Discontinuous Galerkin Time-Domain Scheme to Model Lasing ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ming Dong Liang Chen Ran Zhao Hakan Bagci Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Electronic Science and Engineering Univesirty of Electronic Science and Technology of China (UESTC) Chengdu China

A discontinuous Galerkin time-domain scheme is formulated and implemented to analyze three-dimensional transient lasing dynamics. The proposed scheme solves a coupled system of the Maxwell and the rate equations. The atomic transitions through different energy levels are quantum-mechanically described by a four-level two-electron model, while the electro-magnetic interactions are treated using the Maxwell equations. The resulting solver accounts for the Pauli Exclusion Principle and permits robust simulation of lasing dynamics under optical pumping. Numerical results are presented to demonstrate the applicability and the accuracy of the proposed scheme.

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization 6

Diabetes Classification Problem with CatBoost Method and Opt...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Kurniawan, Safar Dwi Purwono, Purwono Ma'arif, Alfian Suwarno, Iswanto Politeknik Harapan Bersama Department of Computer Engineering Tegal Indonesia Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. The main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. Data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. This technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. The dataset is divided into 85\% composition for training data and 15\% for test data. At the feature selection stage with Correlation Pearson's, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. The results' accuracy is considered less than optimal because it only produces an accuracy value below 80%, even after optimization with Optuna. It can be seen that there was only an increase of 7\% after optimizing with Optuna. © 2023 IEEE.

关键词： Data mining

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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CLeAR: continual learning on algorithmic reasoning for human-like intelligence 23

CLeAR: continual learning on algorithmic reasoning for human...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bong Gyun Kang HyunGi Kim Dahuin Jung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics. Our tasks of abstract logical concepts, in the form of formal language, can be classified into Chomsky hierarchies based on their difficulty. In this study, we conducted extensive experiments consisting of 15 tasks with various levels of Chomsky hierarchy, ranging from in-hierarchy to inter-hierarchy scenarios. CLeAR not only achieved near zero forgetting but also improved accuracy during following tasks, a phenomenon known as backward transfer, while previous CL methods designed for image classification drastically failed.

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Adaptive Neuro-Fuzzy Inference System As New Real-Time Approach for Parkinson Seizures Prediction 4

Adaptive Neuro-Fuzzy Inference System As New Real-Time Appro...

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4th International Conference on Bio-engineering for Smart Technologies, BioSMART 2021

作者： Daher, Alaa Yassin, Sally Alsamra, Hadi Ali, Hassan Abou Beirut Arab University Biomedical Engineering Program Faculty of Engineering Electrical and Computer Engineering Department Debbieh Lebanon

ISBN: (纸本)9781665408103

Parkinson's disease, as a definition, is a neurological condition that affects the brain and causes tremors, stiffness, and difficulties walking, balancing, and coordinating. Symptoms of Parkinson's disease normally appear gradually and worsen with time. People with Parkinson's disease may have difficulties walking and speaking as the condition develops. Numerous recent studies have shown a direct association between Parkinson's disease and the incident of having epileptic seizures, which is defined to be a burst of the uncontrollable electrical activity of the brain cells, that is associated with an increased risk of sudden unexplained deaths. This project aims to obtain a real-time seizure prediction system for Parkinson's disease patients based on the electroencephalogram (EEG) signals, enabling the detection of a seizure before it happens. This will hopefully save them from risky situations or sudden death, as they will be alerted and have the time enabling them to be prepared and take the needed precautions and steps to prevent the seizure from happening. For this project, we've used the Neural Network and the ANFIS ('udaptive neuro-fuzzy inference system '') to process and analyze the electroencephalogram (EEG) data signals of the Parkinson patients to detect seizures starting point. © 2021 IEEE.

关键词： Brain

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Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

Classification and tracking of items on a moving conveyor be...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Angelo Leite Medeiros De Góes Adrião Duarte Dória Neto Matheus Gomes Diniz Andrade Thiago Theiry De Oliveira Electrical and Computer Engineering Postgraduate Program Federal University of Rio Grande do Norte Natal Brazil Computer Engineering Federal University of Rio Grande do Norte Natal Brazil

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process.

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Gain Enhancement of Antenna-on-Chip at 94 GHz with an Integrated Artificial Magnetic Conductor for 6G System-on-Chip

Gain Enhancement of Antenna-on-Chip at 94 GHz with an Integr...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Yiyang Yu Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

Silicon-based complementary metal oxide semiconductor (CMOS) process has become one of the most popular processes to realize system-on-chip (SoC). However, as one of the essential components of wireless SoC, antennas are typically suffering from the poor radiation because of the highly conductive silicon substrate. Such antennas are known as antenna-on-chip (AoC). To enhance the radiation performance of AoC, artificial magnetic conductors (AMC) with double periodic strip structure layers has been proposed in this paper that can not only provide in-phase reflection but also isolate the antenna from the lossy silicon substrate. The proposed AMC shows a gain enhancement of 4.5 dB. The AMC-backed AoC is well-matched within 77-125 GHz and provides a boresight gain of 2 dBi at 94 GHz.

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