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Design and Implementation of an IoT-based Emergency Alert and GPS Tracking System using MQTT and GSM/GPS Module

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Design and Implementation of an IoT-based Emergency Alert an...

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： P. Chinnasamy Pranavdev P. S Chintha Sivakrishnaiah T. Sathiya Irfan Alam Divya Priya Degala Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of MCA Annamacharya University Rajampet India Department of CSE Sona College of Technology Salem Tamilnadu India Cyber Security and Digital Forensics Division SCAI VIT Bhopal University Sehore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

An optimally optimized emergency response system can prove to be lifesaving by reducing response time during the state of emergency. This paper proposes an IoT-based emergency alert and GPS tracking system implemented on an ESP32 microcontroller and an A9G GSM/GPS module. The system has a continuous log of user position and device battery level and pushes data in intervals through MQTT to the Adafruit IO platform for remote monitoring. To achieve best battery life, the A9G module goes into low power mode in idle mode but keeps GPS monitoring running. One more feature highlighted is the SOS button, which makes an emergency call by sending the user's location as an SMS and calling a pre-configured contact number. The system also maintains data integrity with automatic reconnect of MQTT broker when it loses connection. Low power and battery portable, the system is applicable in personal protection, home health care for elderly, outdoor adventure, and remote area surveillance. The article explains hardware design, software process, MQTT-based data communication, and testing of the performance of the system based on precision, network, and response to emergencies. The results verify reliability and cost saving of the system in real-time monitoring and reporting of emergencies. With its energy-saving and usability design, it helps in the development of IoT-backed safety solutions for real-time tracking and emergency response in case of a need.

关键词： Safety devices Protocols Emergency services Real-time systems Batteries Time factors Older adults Usability Global Positioning System Testing

The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

Evidence-aware multi-modal data fusion and its application to total knee replacement prediction

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arXiv 2023年

作者： Liu, Xinwen Wang, Jing Zhou, S. Kevin Engstrom, Craig Chandra, Shekhar S. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be opposite, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to consider the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multi-modal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster’s combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show the superiority of the proposed fusion strategy on various backbone models. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Sparse-view CT Reconstruction with 3D Gaussian Volumetric Representation

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arXiv 2023年

作者： Li, Yingtai Fu, Xueming Zhao, Shang Jin, Ruiyang Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Sparse-view CT is a promising strategy for reducing the radiation dose of traditional CT scans, but reconstructing high-quality images from incomplete and noisy data is challenging. Recently, 3D Gaussian has been applied to model complex natural scenes, demonstrating fast convergence and better rendering of novel views compared to implicit neural representations (INRs). Taking inspiration from the successful application of 3D Gaussians in natural scene modeling and novel view synthesis, we investigate their potential for sparse-view CT reconstruction. We leverage prior information from the filtered-backprojection reconstructed image to initialize the Gaussians;and update their parameters via comparing difference in the projection space. Performance is further enhanced by adaptive density control. Compared to INRs, 3D Gaussians benefit more from prior information to explicitly bypass learning in void spaces and allocate the capacity efficiently, accelerating convergence. 3D Gaussians also efficiently learn high-frequency details. Trained in a self-supervised manner, 3D Gaussians avoid the need for large-scale paired data. Our experiments on the AAPM-Mayo dataset demonstrate that 3D Gaussians can provide superior performance compared to INR-based methods. This work is in progress, and the code will be publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Computerized tomography

Full description of dipole orientation in organic light-emitting diodes

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Chinese Optics Letters 2023年第2期21卷 92-98页

作者：范灵杰赵茂雄楚娇沈唐尧郑敏嘉关放殷海玮石磊资剑 Department of Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and State Key Laboratory of Surface PhysicsFudan UniversityShanghai 200433China Shanghai Engineering Research Center of Optical Metrology for Nano-fabrication(SERCOM) Shanghai 200433China Institute for Nanoelectronic Devices and Quantum Computing Fudan UniversityShanghai 200438China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China

Considerable progress has been made in organic light-emitting diodes(OLEDs)to achieve high external quantum efficiency,among which dipole orientation has a remarkable *** most cases,the radiation of the dipoles in OLEDs is theoretically predicted with only one orientation parameter to match with corresponding ***,we develop a new theory with three orientation parameters to fully describe the relationship between dipole orientation and power ***,we design an optimal test structure for measuring all three orientation *** three orientation parameters could be retrieved from non-polarized *** theory provides a universal plot of dipole orientations in OLEDs,paving the way for designing more complicated OLED devices.

关键词： organic light-emitting diodes dipole orientation Fourier series expansion

A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

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A Cooperative Co-Evolution Algorithm with Variable-Importanc...

Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis

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arXiv 2025年

作者： Ong, Keane Mao, Rui Xing, Frank Satapathy, Ranjan Sulaeman, Johan Cambria, Erik Mengaldo, Gianmarco College of Design and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore School of Computing National University of Singapore 13 Computing Drive 117417 Singapore Asian Institute of Digital Finance National University of Singapore Innovation 4.0 3 Research Link #04-03 117602 Singapore College of Computing and Data Science Nanyang Technological University 50 Nanyang Ave 639798 Singapore Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way #16-16 Connexis 138632 Singapore Sustainable Green Finance Institute National University of Singapore Innovation 4.0 3 Research Link #02-02 117602 Singapore NUS Business School National University of Singapore Mochtar Riady Building 15 Kent Ridge Dr Mocthar Riady Building 119245 Singapore Imperial College London United Kingdom

Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the complexity and volume of corporate sustainability data (i.e. sustainability disclosures), not least by the effectiveness of the NLP tools used to analyse them. To this end, we identify three primary challenges – immateriality, complexity, and subjectivity, that exacerbate the difficulty of extracting insights from sustainability disclosures. To address these issues, we introduce ESGSenticNet, a publicly available knowledge base for sustainability analysis. ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy. This approach culminates in a structured knowledge base of 44k knowledge triplets – (‘halve carbon emission’, supports, ‘emissions control’), for effective sustainability analysis. Experiments indicate that ESGSenticNet, when deployed as a lexical method, more effectively captures relevant and actionable sustainability information from sustainability disclosures compared to state of the art baselines. Besides capturing a high number of unique ESG topic terms, ESGSenticNet outperforms baselines on the ESG relatedness and ESG action orientation of these terms by 26% and 31% respectively. These metrics describe the extent to which topic terms are related to ESG, and depict an action toward ESG. Moreover, when deployed as a lexical method, ESGSenticNet does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders. © 2025, CC BY.

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REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT)

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REC-NN: A reconstruction error compensation neural network f...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ruian Qin Adan Jafet Garcia Inda Zhongchao Zhou Yukihiro Enomoto Tianyi Yang Nevrez İmamoğlu Jose Gomez-Tames Shaoying Huang Wenwei Yu Department of Medical Engineering Chiba University Chiba Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo Center for Frontier Medical Engineering Chiba University Chiba Department of Surgery National University of Singapore Singapore Engineering Product Development Department Singapore University of Technology and Design

Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems of MREPT are being solved progressively through recent efforts, the loss of tissue contrast emerges as an obstacle to the clinical applications of MREPT. To solve the problem, we propose a reconstruction error compensation neural network scheme (REC-NN) for a typical analytic MREPT method, Stab-EPT. Two NN structures: one with only ResNet blocks, and the other hybridizing ResNet blocks with an encoder-decoder structure. Results of experiments with digital brain phantoms show that, compared with Stab-EPT, and conventional NN based reconstruction, REC-NN improves both reconstruction accuracy and tissue contrast. It is found that, the encoder-decoder structure could improve the compensation accuracy of EPs in homogeneous region but showed worse reconstruction than only ResNet structure for tumorous tissues unseen in the training samples. Future research is required to address overcompensation problems, optimization of NN structure and application to clinical data.

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The effect of neural cell integrated into 3D co-axial bioprinted BMMSC structures during osteogenesis

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Regenerative Biomaterials 2021年第5期8卷 87-94页

作者： Yi Zhang Haiyan Chen Xiaoyan Long Tao Xu Precision Medicine and Healthcare Research Center Tsinghua-Berkeley Shenzhen Institute(TBSI)Tsinghua UniversityShenzhen 518055People’s Republic of China East China Institute of Digital Medical Engineering Shangrao 334000People’s Republic of China Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing Department of Mechanical EngineeringTsinghua UniversityBeijing 100084People’s Republic of China Key Laboratory for Advanced Materials Processing Technology Ministry of EducationDepartment of Mechanical EngineeringTsinghua UniversityBeijing 100084People’s Republic of China

A three-dimensional(3D)bioprinting is a new strategy for fabricating 3D cell-laden constructs that mimic the structural and functional characteristics of various tissues and provides a similar architecture and microenvironment of the native ***,there are few reported studies on the neural function properties of bioengineered bone ***,this study was aimed at investigating the effects of neural cell integration into 3D bioprinted bone *** bioprinted hydrogel constructs could maintain long-term cell survival,support cell growth for human bone marrow-derived mesenchymal stem cells(BMMSCs),reduce cell surface biomarkers of stemness,and enhance orthopedic differentiation with higher expression of osteogenesis-related genes,including osteopontin(OPN)and bone morphogenetic *** importantly,the bioprinted constructs with neural cell integration indicated higher OPN gene and secretory alkaline phosphatase *** results suggested that the innervation in bioprinted bone constructs can accelerate the differentiation and maturation of bone development and provide patients with an option for accelerated bone function restoration.

关键词： innervation 3D bioprinting co-axial BMMSC differentiation osteogenesis