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6G Networking (6GNet), International Conference on

作者： John Sengendo Fabrizio Granelli Dept. of Information Engineering and Computer Science University of Trento Trento ITALY

ISBN: (数字)9798350378597

ISBN: (纸本)9798350378603

The Network Digital Twin is emerging as a promising technology for future networks, including 6G, as it allows to gain deep knowledge of its Physical Twin and to perform “what-if“ analyses in a controlled environment. However, while the concept of Digital Twin reached maturity in other fields (such as manufacturing), still the application of this concept to networks presents some relevant challenges. This paper studies the problem of synchronization between the Digital and the Physical Twin, a key functionality to enable the Digital Twin to accurately replicate the behaviour of the Physical Twin. Indeed, accurate synchronization is challenging due to the amount of data to exchange and the delays due to acquisition, modeling and transmission of the related information. The paper analyzes the delay chain in the synchronization process and introduces the concept of “negative delay” through accurate prediction in order to minimize the synchronization delay and enable real-time or “near” real-time synchronization between the Physical and the Digital Twin.

关键词： 6G mobile communication Knowledge engineering Accuracy Buildings Throughput Real-time systems Digital twins Delays Manufacturing Synchronization

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In-memory Machine Learning using Adaptive Multivariate Decision Trees and Memristors

In-memory Machine Learning using Adaptive Multivariate Decis...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Akash Chavan Pranav Sinha Sunny Raj Dept. of Computer Science and Engineering Oakland University Rochester MI USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

We introduce a framework to design in-memory decision tree machine-learning (ML) circuits using memristor crossbars. Decision trees (DTs) offer many advantages over neural networks, such as enhanced energy efficiency, interpretability, safety, privacy, and speed, along with reduced dependence on extensive training data. We propose an adaptive multivariate decision tree (AMDT) training algorithm, which constructs decision trees that incorporate both univariate and multivariate features, facilitating the creation of higher accuracy and energy-efficient crossbar designs compared to the state-of-the-art (SOTA). Our circuits are realized using pure memristor crossbars, requiring just one memristor per cell and no transistors while employing sneak-paths for flow-based in-memory computations. In comparison to the SOTA, our approach produces designs that are, on average, 4% more accurate and require 12.6% lower energy.

关键词： Training Accuracy Circuits Memristors Training data Machine learning Energy efficiency

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Quantum Machine Learning for Anomaly Detection in Consumer Electronics

Quantum Machine Learning for Anomaly Detection in Consumer E...

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IEEE computer Society Annual Symposium on VLSI

作者： Sounak Bhowmik Himanshu Thaplival Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.

关键词： Quantum computing Machine learning algorithms Identity theft Finance Machine learning Very large scale integration Network intrusion

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Automating Requirements Review in the Automotive Sector: A Tailored AI Approach

Automating Requirements Review in the Automotive Sector: A T...

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IEEE International Conference on Requirements engineering

作者： Sivajeet Chand Chang Li Cristina Martinez Montes Beatriz Cabrero-Daniel Jennifer Horkoff Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in de... 详细信息

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

关键词： Reviews Software Requirements engineering Artificial intelligence Automotive engineering

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Advancing Multi-Agent Systems Integrating Federated Learning with Deep Reinforcement Learning: A Survey

Advancing Multi-Agent Systems Integrating Federated Learning...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Jaemin Kim Gahyun Kim Seonghun Hong Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

Recent advancements in the field of Deep Reinforcement Learning (DRL), such as the development of algorithms like Deep Q-Network, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO), have been significant. However, these algorithms encounter data privacy issues in multi-agent environments. This paper investigates the latest research trends in applying federated learning to address data privacy issues in multi-agent systems within deep reinforcement learning. It delves into various approaches for implementing federated learning in these systems, examines the challenges faced, and explores potential solutions to enhance privacy while maintaining or improving the performance of DRL algorithms in multi-agent setting.

关键词： Surveys data privacy Privacy Federated learning Deep reinforcement learning Market research Optimization

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Modular Soft-switched PV Converter with an Efficiency Optimization Scheme for High Frequency Linking Power Balancers

Modular Soft-switched PV Converter with an Efficiency Optimi...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Kajanan Kanathipan John Lam Dept. of Electrical Engineering and Computer Science York University Toronto Canada

A modular photovoltaic (PV) step-up converter with embedded high frequency power balancers that utilize an interlinking controller capable of power efficiency optimization over a wide operating range for Medium Voltage (MV) DC distribution is proposed in this paper. The converter utilizes an integrated boost-CLL resonant circuit with power balancing active voltage quadruplers (AVQ) to achieve step-up voltage conversion, individual maximum power point tracking (MPPT) and modular power balancing. The proposed interlinking power balancer controller optimizes the transmission of mismatched power while simultaneously achieving soft-switching operation on all AVQ circuits. The modular nature of the system allows for additional modules to be added without requiring parameter changes to the existing system. Analysis of the proposed converter and power balancing technique along with 10kW, 10kV-output system simulation and preliminary results on a scaled down 500W, 850V-output system are provided to highlight the features of the proposed system.

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Developing a Self-Explanatory Transformer

Developing a Self-Explanatory Transformer

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Rasha Karakchi Ryan Karbowniczak Dept. of Computer Science and Engineering University of South Carolina Columbia USA

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

While IoT devices provide significant benefits, their rapid growth results in larger data volumes, increased complexity, and higher security risks. To manage these issues, techniques like encryption, compression, and mapping are used to process data efficiently and securely. General-purpose and AI platforms handle these tasks well, but mapping in natural language processing is often slowed by training times. This work explores a self-explanatory, training-free mapping transformer based on non-deterministic finite automata, designed for Field-Programmable Gate Arrays (FPGAs). Besides highlighting the advantages of this proposed approach in providing real-time, cost-effective processing and dataset-loading, we also address the challenges and considerations for enhancing the design in future iterations.

关键词： Training Automata Transformers Real-time systems Natural language processing Encryption Security Internet of Things Field programmable gate arrays Edge computing

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An Efficient Deep Learning Approach for Jute Pest Classification Using Transfer Learning

An Efficient Deep Learning Approach for Jute Pest Classifica...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Muhammad Tanvirul Islam Md. Sadekur Rahman Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Jute is considered as one of the most vital crops in the world. For some countries jute is the principal source of earnings and GDP. One of the primary elements influencing jute yield is jute pests. Accurate pest identification makes it possible to take prompt preventative action to minimize financial losses. Considering the fact, to classify jute pests, the study suggests different jute pest classification models, which are based on transfer learning. The best model offers high performance and resilience. A VCI-validated dataset comprising 7235 images has been utilized in the analysis. The dataset encompasses images classified into 17 distinct jute pest classes. The dataset is already divided into three categories train, test and validation. To increase the dataset size, data augmentation is applied to the training set. To improve performance, all the models were integrated with the transfer learning model. VGG 16, ResNetl0l, DenseNet201, InceptionV3, Xception, and MobileN etV2 were used to train the parameters on the publicly available ImageN et dataset followed by some customized dense layers. The models were assessed using different types of metrics, including confusion matrix, F1 score, precision, and recall. Compared to other models DenseNet201 outclassed other models, acquiring 97% accuracy. The fundamental information and technical support for jute pest classification are provided by this study.

关键词： Training Measurement Deep learning Economic indicators Transfer learning Crops data augmentation

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Identification of Black Fungus Diseases Using CNN and Transfer-Learning Approach 22

Identification of Black Fungus Diseases Using CNN and Transf...

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2nd International Conference on Computing Advancements: Age of Computing and Augmented Life, ICCA 2022

作者： Hasan, Md Imran Mahbub, Nosin Ibna Sarkar, Bappa Dept. of Computer Science and Engineering Islamic University Kushtia Bangladesh Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (纸本)9781450397346

Black Fungus is a dangerous fungal illness, commonly referred to as 'mucormycosis', usually infecting uncompromising people. Mucormycosis is generally rare, affecting less than two individuals per million each year, but it is currently 80 times more frequent in India. People of all ages, including prematurity infants, may be impacted. To minimize the time and effort required by medical experts to investigate black fungus disease and enhance the consistency of image identification of black fungus infections, we suggested a transfer learning based CNN model as the dataset for the black fungal disease is limited. The experimental outcomes of several different architectures, including VGG16, InceptionV3, and Xception, are compared and analyzed in this study, and it is observed that Xception has the best performance, with 97.92% training accuracy and 95.60% testing accuracy. © 2022 ACM.

关键词： Image enhancement

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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