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Braille–Latin conversion using memristive bidirectional associative memory neural network

Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12511-12534页

作者： Vaidyaraman, Jayasri Thyagarajan, Abitha K. Shruthi, S. Ravi, V. School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The successful implementation of ANNs in software—particularly in the fields of deep learning and machine learning—has spiked an interest in designing hardware architectures that are custom-made to implement ANNs. Several categories of ANNs exist. The two-layer bidirectional associative memory (BAM) is a particular class of hetero-associative memory networks that is extremely efficient and exhibits good performance for storing and retrieving pattern pairs. The memristor is a novel hardware element that is well-suited to modelling neural synapses because it exhibits tunable resistance. In this work, in order to create a device that can perform Braille–Latin conversion, we have implemented a circuit realization of a BAM neural network. The implemented hardware BAM uses a memristor crossbar array for modelling neural synapses and a neuron circuit comprising an I-to-V converter (resistor), voltage comparator, D flip-flop, and inverter. The efficiency of the implemented hardware BAM was tested initially using 2 × 2 and 3 × 3 patterns. Upon successfully verifying the ability of the implemented BAM to store and retrieve simple pattern pairs, it was trained for a pattern-recognition application, namely mapping Braille alphabets to their Latin counterparts and vice versa. The performance of the implemented BAM network is robust even on the introduction of noise. The application can recognize the input patterns with accuracies of 100% in either direction when tested with up to 30% noise. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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Investigation on radiated emissions of electric aircraft at airports

Investigation on radiated emissions of electric aircraft at ...

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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Yan, Jiexiong Johansson, Jonny Ekman, Jonas Nilsson, Andreas Wisten, Ake Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350324006

Electric aircraft are gaining popularity due to their zero-carbon emission, low noise level and low operational cost. Current study regarding electric aircraft centers on safety as well as battery durability and little light has been shed on electromagnetic compatibility (EMC). Electric aircraft, together with their charging stations, are complex systems and their existence at an airport might pose the risk for interference of other electric equipment, for example, communication systems. This paper presents preliminary measurements on electromagnetic emissions from an electric aircraft and its charging station at an airport. Radiated emission measurements were performed from 30 MHz to 1 GHz and the results suggest that the aircraft emits energy mainly below 200 MHz during the charging process and that the general radiation level at the test site challenges the limits for class B equipment in EN 55032. However, the electric aircraft and the charging station do not seem to have influence on the communication systems at the test site. The results could serve as a guidance in terms of charging station installations at airports and the establishment of EMC related regulations about electric aircraft. © 2023 IEEE.

关键词： Electromagnetic compatibility

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Placement of Charging Sites for Off-Service Battery Electric Bus in Transit Networks 8

Placement of Charging Sites for Off-Service Battery Electric...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Sobhani, Ehsan Yassine, Abdulsalam Ameli, Amir Riahinia, Shahin Department of Computer Science Lakehead University Thunder BayON Canada Department of Software Engineering Lakehead University Thunder BayON Canada Department of Electrical and Computer Engineering Lakehead University Thunder BayON Canada

ISBN: (纸本)9798350382150

Battery electric buses (BEBs) have received significant recognition as an environmentally conscious and sustainable means of transportation. Placement of charging stations to ensuring efficient and consistent charging of BEBs is a significant challenge in an electrified public transit network. This paper introduces the Constrained Greedy Clustering (CGC) algorithm, a data-driven approach for hierarchically determining optimal charging placement locations. The algorithm aims to minimize energy consumption and optimize charging load distribution in transit networks using clustering techniques. This improves the placement of charging sites for off-service BEBs in transit networks, aiming to minimize energy consumption and balance the charging sites’ load. ©2024 IEEE.

关键词： Energy efficiency

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Hydrogeological Risk Analysis Using computer Vision Techniques 9

Hydrogeological Risk Analysis Using Computer Vision Techniqu...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Borzì, Maria Grazia Beritelli, Ludovica Puglisi, Valerio Francesco Avanzato, Roberta Beritelli, Francesco Bellino, Salvatore Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Computer Science University of Catania Catania Italy

Flood events constitute one of the most serious natural threats, causing significant damage to the environment and endangering human life. In response to this issue, we propose an innovative system for automated video analysis of flood events and classification of criticality levels using computer vision. Our approach is based on the YOLOv8 neural architecture, known for its speed and effectiveness in detecting and classifying objects in complex scenes. The system is capable of classifying 5 levels of criticality, from level zero indicating no criticality to level 5 indicating maximum criticality, allowing rapid and accurate assessment of the situation. Experimental results were conducted by considering two real scenarios. The accuracy performance obtained on the 5 criticality classes averaged 98.02%. This study contributes to the advancement of natural disaster monitoring and prevention technologies by providing an efficient and reliable method to assess hydrogeological risk and protect communities from flooding. © 2023 Copyright for this paper by its authors.

关键词： Floods

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Evaluation of Federated Learning Techniques on Edge Devices Using Synthetic Medical Imaging Datasets

Evaluation of Federated Learning Techniques on Edge Devices ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Alhonainy, Ahmad Rao, Praveen University of Missouri Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350359527

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances disease diagnosis using diverse medical datasets. However, learning generalizable machine learning models requires data from multiple data providers and should not compromise patient privacy to abide by federal laws. Federated learning (FL) is an attractive approach of learning such models from different data providers without exchanging the actual data owned by the providers. In certain medical scenarios, there is limited labeled data to train models via FL. Therefore, in this paper, we explore the effectiveness of synthetic medical imaging datasets for training deep learning models using FL. We specifically focus on FL using edge devices that have limited computing and storage capacity. Our federated learning environment was implemented using the open source Flower framework and a network of edge devices. We selected a publicly available histopathology image dataset related to lung and colon cancer. We evaluated the performance of three well-known deep learning models, namely, DenseNet121, EfficientNetB0, and MobileNetV2 by training them on both real and synthetic images. We observed that DenseNet121 consistently performed the best. For example, it achieved an accuracy of 98% when trained on both real and synthetic images for colon cancer. Thus, synthetic data can be effectively used to train deep learning models via FL when real data are limited. © 2023 IEEE.

关键词： Medical imaging

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Mobile-Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1845-1853页

作者： Liu, Weiqi Arif Hossain, Mohammad Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid under Data Scarcity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2482-2492页

作者： Xu, Jiangjiao Li, Ke Li, Dongdong Shanghai University of Electric Power Department of Electrical Engineering Shanghai20090 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Microgrid can improve greenhouse gas emissions and reduce operational costs. To forecast both energy generation and load demand, time-series prediction has been a key tool in real-time control and optimization. Developing an adequate predictive model is difficult when there is a lack of historical data. Moreover, hyperparameters have a tangible impact on the performance of machine learning models. Bearing these considerations in mind, this article develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bilevel programming perspective. Specifically, a lower level metalearner helps mitigate the small data challenge, whereas an upper level optimization optimizes both hyperparameters for lower and upper level learners. Note that the proposed framework is designed to accommodate a wide range of machine learning methods, allowing for easy integration through a plug-in mechanism. Comprehensive experiments demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML to search for a high-performance few-shot learning pipeline for various energy sources. © 2020 IEEE.

关键词： Forecasting

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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UAV-Assisted Internet of Vehicles over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

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IEEE Internet of Things Magazine 2023年第3期6卷 78-84页

作者： Su, Yuhan Liwang, Minghui Chen, Zhong Wang, Xianbin School of Electronic Science and Engineering Xiamen University China School of Informatics Xiamen University China Western University Department of Electrical and Computer Engineering London Canada

Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated. © 2018 IEEE.

关键词： Wi-Fi

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