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Development of smart energy monitoring using NB-IOT and cloud

Measurement: Sensors 2023年 29卷

作者： Siva Balan, R.V. Gouri, M.S. Senthilnathan, T. Gondkar, Sureha Raju Gondar, Raju R. Loveline Zeema, J. Jothikumar, R. Bangalore India Department of Computer Science and Engineering Baselios Mathews II College of Engineering Lake View Muthupilakadu Sasthamcotta Kerala Kollam690 520 India Department of Electronics and Communication and Engineering BMS Institute of Technology and Management Bengaluru560064 India Department of Computer Science and Engineering Shadan College of Engineering and Technology Peeran Cheru Hyderabad India

IoT-based applications are growing in popularity nowadays because they offer effective answers to numerous current problems. In this research, With the aim of decreasing human efforts for monitoring the power units and increasing users' knowledge of excessive electricity usage, an IoT-based electric metre surveillance system utilising an Android platform has been developed. With the help of an Arduino Uno and an optical sensor, the electric analyser pulse is captured. To reduce human mistake and the expense of energy usage, a low-cost wireless network of sensors for digital energy metres is implemented alongside a smartphone application that can autonomously read the metre of the unit. In this research, an intelligent power monitoring system with effective communication modules has been developed to make wise use of the electricity. The controller, NB-IoT connection module, and cloud are the three main components of an IOT-based smart energy metre system. The controller is essential for maintaining the functionality of each component. This solution reduces the need for human involvement in electricity maintenance by connecting energy metres to the cloud using an NB-IoT communication module. The IoT-based metre reading system in the proposed work is created to monitor and analyse the metre reading, and the service provider can cut off the source of electricity whenever the customer fails to pay the monthly bill. It also eliminates the need for human intervention, provides accurate metre reading, and guards against billing errors. The proposed SPM improves the overall accuracy ranges of 7.42, 27.83, and 20% better than DR, OREM, and SLN respectively. © 2023

关键词： Internet of things

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On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

On the (Limited) Generalization of MasterFace Attacks and It...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Philipp Terhörst Florian Bierbaum Marco Huber Naser Damer Florian Kirchbuchner Kiran Raja Arjan Kuijper Norwegian University of Science and Technology Gjvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9781665463959

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art face recognition models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. Future works might analyse the utility of MasterFaces for understanding and enhancing the robustness of face recognition models.

关键词： Training Analytical models Protocols Face recognition Sociology Market research Robustness

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Determinants of Cybercrime Victimization: Experiences and Multi-stage Recommendations from a Survey in Cameroon 14th

Determinants of Cybercrime Victimization: Experiences and Mu...

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14th EAI International Conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Ntsama, Jean Emmanuel Tchakounte, Franklin Tchakounte Tchuimi, Dimitri Faissal, Ahmadou Fotso Kuate, Franck Arnaud Effa, Joseph Yves Udagepola, Kalum Priyanath Atemkeng, Marcellin Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Yaoundé Cameroon Department of Human Resource Economics Faculty of Economics and Management University of Yaoundé II SOA Yaoundé Cameroon Department of Physics Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia Department of Mathematics Rhodes University Makhanda South Africa

ISBN: (纸本)9783031348952

Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and user’s vulnerability to attacks catalyze their success. Several surveys have been conducted to determine the factors favoring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication Technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers and a theoretical framework has been provided with several stages that could be followed to contain cybercrime. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Statistical tests

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DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading

arXiv

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arXiv 2024年

作者： Wu, Tong Sun, Jia-Mu Lai, Yu-Kun Ma, Yuewen Kobbelt, Leif Gao, Lin Huazhong University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China Nanyang Technological University Singapore The University of Karlsruhe Germany The Computer Graphics Group The RWTH Aachen University Germany Sichuan University China

Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision. Neural radiance fields (NeRFs) can achieve realistic reconstruction and editing results but suffer from inefficiency in rendering. Gaussian splatting significantly accelerates rendering by rasterizing Gaussian ellipsoids. However, Gaussian splatting utilizes a single Spherical Harmonic (SH) function to model both texture and lighting, limiting independent editing capabilities of these components. Recently, attempts have been made to decouple texture and lighting with the Gaussian splatting representation but may fail to produce plausible geometry and decomposition results on reflective scenes. Additionally, the forward shading technique they employ introduces noticeable blending artifacts during relighting, as the geometry attributes of Gaussians are optimized under the original illumination and may not be suitable for novel lighting conditions. To address these issues, we introduce DeferredGS, a method for decoupling and editing the Gaussian splatting representation using deferred shading. To achieve successful decoupling, we model the illumination with a learnable environment map and define additional attributes such as texture parameters and normal direction on Gaussians, where the normal is distilled from a jointly trained signed distance function. More importantly, we apply deferred shading, resulting in more realistic relighting effects compared to previous methods. Both qualitative and quantitative experiments demonstrate the superior performance of DeferredGS in novel view synthesis and editing tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Lighting

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CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images

arXiv

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arXiv 2024年

作者： Jin, Yuan Ma, Gege Chen, Geng Lyu, Tianling Egger, Jan Lyu, Junhui Zhang, Shaoting Zhu, Wentao Zhejiang Lab 311121 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China The Zhejiang University School of Medicine Sir Run Run Shaw Hospital Hangzhou310016 China Shanghai Artificial Intelligence Laboratory Shanghai200120 China

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to limited diagnostic accuracy. To this end, we propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images, i.e., CT and pathological images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets as well as independent CT image sets, and consequently optimize the pathology-related feature extractions from CT images. This adaptive learning approach enhances the flexibility in processing multi-dimensional and multi-modality datasets and results in performance elevating in the model testing phase. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training, and thereby helps to explore the "gold standard" pathological information from the corresponding CT scans. To evaluate the effectiveness, adaptability, and generalization ability of our model, we conducted extensive experiments on a large-scale multi-center dataset and compared our model with a series of state-of-the-art classification models. The experimental results demonstrated the superiority of our model for lung cancer subtype classification, showcasing significant improvements in accuracy metrics such as ACC, AUC, and F1-score. Copyright © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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Advanced Deep Reinforcement Learning Strategies for Enhanced Autonomous Vehicle Navigation Systems

Advanced Deep Reinforcement Learning Strategies for Enhanced...

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computer, Communication and Control (IC4), International Conference on

作者： M Dhinakaran R.Thalapathi Rajasekaran V. Balaji V. Aarthi S. Ambika Department of Electronics and Communication Engineering Galgotias College of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

The objective of this research is to investigate more sophisticated techniques in Deep Reinforcement Learning (DRL) for the purpose of improving navigation systems for autonomous vehicles. The results of our research indicate that the success rates of DRL algorithms are encouraging: DQN (75.2%), PPO (82.6%), and TRPO (78.1%). The success of navigation is highly influenced by optimisation factors such as parameter tuning (87.4%), exploration-exploitation balance (85.1%), and reward shaping (88.9%). The generalisation score for transfer learning is rather good (92.3%), while the safety modules are designed to make the system safer. These findings highlight the significance of algorithm selection, parameter optimisation, and advanced techniques in the process of optimising navigation for autonomous vehicles, hence establishing essential benchmarks for future developments.

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhenrong Deng Jinglin Zhang Yannan Xiao Shiwei Wei Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models.

关键词： Training Accuracy Translation Semantics Refining Named entity recognition Syntactics Feature extraction Solids Robots

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Decentralized Acceleration-Based Bird-Inspired Flocking

Decentralized Acceleration-Based Bird-Inspired Flocking

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Iacone Erwin Lejeune Tiziano Manoni Sabato Manfredi Dario Albani Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE Department of Computer Science Vrije Universiteit Amsterdam The Netherlands Department Electrical Engineering and Information Technology Federico II University Naples Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the following, we analyze and discuss the implementation of a novel approach for distributed flocking behavior applied to a group of Uncrewed Aerial Vehicle (UAV)s, also referred to as drones. Inspired by natural flocking phenomena observed in birds, which demonstrate coordinated movement in response to internal and external stimuli, we tackle the problem of robust and dynamic aerial motion for robots and design a control law based on a novel physical model. In contrast to previous works that rely on velocity or position-based references, this approach leverages an acceleration-based law to describe the collective dynamics of many interacting particles. As observed in the following, a third-order control possesses several advantages compared to first or second-order control, such as smoother transitions, better force balancing, and more responsive and dynamic behaviors. These advantages are thoroughly analyzed in the following, thanks to physics-based realistic simulations and field experiments with medium-sized UAVs in an unstructured outdoor environment.

关键词： Navigation Robot kinematics Perturbation methods Dynamics Life estimation Autonomous aerial vehicles Trajectory Vehicle dynamics Drones Testing

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Design Limitations in Oxide-Based Memristive Ternary Content Addressable Memories

Design Limitations in Oxide-Based Memristive Ternary Content...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Leon Brackmann Tobias Ziegler Atousa Jafari Dirk J. Wouters Mehdi Tahoori Stephan Menzel Institute for Electronic Materials II RWTH Aachen Aachen Germany Department of Computer Science Karlsruhe Insitute of Technology Karlsruhe Germany Peter-Grünberg Institute 7 FZ Juelich Juelich Germany

Memristive devices offer energy and area efficient non-volatile data storage for data-intense Ternary Content Ad-dressable Memory (TCAM) architectures. However, depending on the storage implementation in the bitcell design, the matching functionality shows multiple undesired discharge effects leading to false look-up results. In particular, the ternary storage suffers during the look-up operation from a poor resistance ratio, match-line leakage and device variabilities. In this paper, we investigate the inherent, design-dependent limitations in the ternary state storage capability due to different memristive TCAM bitcell design parameters and device variabilities. We test these limits based on variability-aware device simulations and isolate crucial parameters for the optimization of memristive TCAMs.

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A driver-in-the-loop co-simulation framework for testing predictive EDAS for commercial vehicles in urban environments

A driver-in-the-loop co-simulation framework for testing pre...

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6th International Commercial Vehicle Technology Symposium, CVT 2020

作者： Chada, Sai Krishna Gőrges, Daniel Ebert, Achim Teutsch, Roman Division of Electromobility Department of Electrical and Computer Engineering University of Kaiserslautern Kaiserslautern67663 Germany Computer Graphics and HCI Group Department of Computer Science University of Kaiserslautern Kaiserslautern67663 Germany Institute for Mechanical and Automotive Engineering Department of Mechanical and Process Engineering University of Kaiserslautern Kaiserslautern67663 Germany

ISBN: (纸本)9783658297169

To experience realistic driving scenarios in the virtual simulation environment, strengths of both the traffic simulation and the vehicle simulation tools must be combined. In this paper, a driver-in-the-loop co-simulation framework for testing predictive eco-driving assistance systems (EDAS) for commercial vehicles in urban environments is proposed. The co-simulation is performed by exchanging information between IPG TruckMaker, a vehicle simulation software tailored for commercial vehicles, and SUMO, an open-source software for microscopic traffic simulation. To avoid delays during the data exchange process, time-steps of both the tools are synchronized. The co-simulation environment replicates a bus route in the city of Kaiserslautern along with the corresponding bus stops, traffic lights, road speed limits, pedestrian crossings and road elevation. The stochastic traffic and pedestrian flow in the road network is generated using SUMO. In this work, the co-simulation framework is extended by introducing the driver into the simulation loop. This is achieved by coupling the vehicle simulation tool with a driving simulator, in which the driver controls the ego-vehicle. The proposed framework will serve to test various use-cases of predictive eco-driving assistant systems (EDAS). © Der/die Autor(en), exklusiv lizenziert durch.

关键词： Open source software

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