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eSMARTGreen (ESG): A Scalable IoT-Based Architecture for Multi-Greenhouse Management

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Sustainable Computing: Informatics and systems 2025年

作者： Fatima Abou-Mehdi-Hassani Atef Zaguia Darine Amyed Hassan Ait Bouh Abdelhak Mkhida Moulay Ismail University ENSAM Meknes Laboratory of Mechanics Mechatronics and control(L2MC) Marjane 2 PO Box 15290 Al Mansour Road Agouray 50500 Meknes Morocco Department of Computer Science College of Computers and Information Technology Taif University 11099 Taif 21944 Saudi Arabia Institut Supérieur des Sciences Humaines de Jendouba Université de Jendouba Tunisia System Engineering Department École de Technologie Supérieure University of Quebec Montreal Canada

Concerns about agricultural productivity and sustainability have driven the need for smart greenhouse architectures. However, signifiant challenges remain in ensuring seamless data exchange, interoperability, and efficient management across multiple greenhouses. This paper introduces the eSMARTGreen (ESG) model, a novel IoT-based smart greenhouse architecture designed for scalable multi-greenhouse management. ESG features fault-tolerant, modular, and flexible deployment strategy, integrating a robust decision-making system and an interoperable framework aligned with ISO/IEC 30141 standards. The ESG model was validated through a simulation conducted using CPN Tools across a network of five greenhouses. Performance metrics showed low average latencies (19–25 ms) and reception rates of up to 72%, confirming ESG’s responsiveness and communication efficiency under diverse operational conditions. By facilitating seamless coordination and automation, ESG contributes to greater efficiency and sustainability in smart farming. Future applications of ESG could include predictive maintenance, adaptive climate control, large-scale deployment in agricultural clusters, and integration with renewable energy systems to further enhance sustainability and operational efficiency.

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Deep Learning to Improve Heart Disease Risk Prediction 1st

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Deep Learning to Improve Heart Disease Risk Prediction

1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Sajeev, Shelda Maeder, Anthony Champion, Stephanie Beleigoli, Alline Ton, Cheng Kong, Xianglong Shu, Minglei Flinders Digital Health Research Centre School of Nursing and Health Sciences Flinders University Adelaide Australia Department of Big Data Engineering Technology Research Center of E-Government JinanShandong China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology Jinan China

ISBN: (纸本)9783030333263

Disease prediction based on modeling the correlations between compounded indicator factors is a widely used technique in high incidence chronic disease prevention diagnosis. Predictive models based on personal health information have been developed historically by using simple regression fitting over relatively few factors. Regression approaches have been favored in previous prediction modeling approaches because they are simplest and do not assume any non-linearity in the model for contributions of the chosen factors. In practice, many factors are correlated and have underlying non-linear relationships to the predicted outcome. Deep learning offers a means to construct a more complex modeling approach, along with automation and adaptation. The aim of this paper is to assess the ability of a deep learning model to predict the heart disease incidence using a common benchmark dataset (University of California, Irvine (UCI) dataset). The performance of deep learning model has been compared with four popular machine learning models (two linear and two nonlinear) in predicting the incidence of heart disease using data from 567 participants from two cohorts taken from UCI database. The deep learning model was able to achieve the best accuracy of 94% and an AUC score of 0.964 when compared to other models. The performance of deep learning and nonlinear machine learning models was significantly better compared to the linear machine learning models with increase in the dataset size. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest 15th

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Effective Analysis of Hot Spots in Hub Protein Interfaces Ba...

15th International Conference on Intelligent Computing, ICIC 2019

作者： Lin, Xiaoli Zhou, Fengli Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Information and Engineering Department of City College Wuhan University of Science and Technology Wuhan430083 China

ISBN: (纸本)9783030269685

In the protein-protein interactions, hub proteins are the key factor to maintain stability of protein-protein interactions and exert the protein biological function. Conventional methods mainly focus on the topological structure and gene expression of hub proteins, while we mainly discuss the hot spots of hub protein interfaces. In order to evaluate the performance of the classification models, the importance of feature variables is analyzed by using the average precision descent curve and the average Gini coefficient descent curve. In addition, the margin box-plot is used to measure the certainty of the classification models. The experimental results show that the error rate of random forest method is lower, and our classification model has higher reliability. © 2019, Springer Nature Switzerland AG.

关键词： Gene expression

High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： Electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

A registration-aided domain adaptation network for 3D Point cloud based place recognition

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Distributed machine learning for wireless communication networks: Techniques, architectures, and applications

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arXiv 2020年

作者： Hu, Shuyan Chen, Xiaojing Ni, Wei Hossain, Ekram Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China SydneyNSW2122 Australia Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research. Copyright © 2020, The Authors. All rights reserved.

关键词： Data privacy

A multi-functional reconfigurable metasurface: Electromagnetic design accounting for fabrication aspects

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arXiv 2020年

作者： Pitilakis, Alexandros Tsilipakos, Odysseas Liu, Fu Kossifos, Kypros M. Tasolamprou, Anna C. Kwon, Do-Hoon Mirmoosa, Mohammad Sajjad Manessis, Dionysios Kantartzis, Nikolaos V. Liaskos, Christos Antoniades, Marco A. Georgiou, Julius Soukoulis, Costas M. Kafesaki, Maria Tretyakov, Sergei A. The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece The Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Heraklion71110 Greece The Institute of Electronic Structure and Laser Foundation for Research and Technology Hellas Heraklion Crete71110 Greece The Ames Laboratory-U.S. DOE and Department of Physics and Astronomy Iowa State University AmesIA50011 United States The Department of Electronics and Nanoengineering Aalto University P.O. Box 15500 AaltoFI-00076 Finland The Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electrical Computer & Biomedical Engineering Ryerson University ONM5B 2K3 Canada The Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States System Integration and Interconnection Technologies Fraunhofer IZM Berlin Germany The Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece The University of Ioannina Computer Science Engineering Department Greece

In this paper we present the theoretical considerations and the design evolution of a proof-of-concept reconfigurable metasurface, primarily used as a tunable microwave absorber, but also as a wavefront manipulation and polarization conversion device in reflection. We outline the design evolution and all considerations taken into account, from the selection of patch shape, unit cell size, and substrate, to the topology of the structure that realizes the desired tunability. The presented design conforms to fabrication restrictions and is co-designed to work with an integrated circuit chip for providing tunable complex loads to the metasurface, using a commercially available semiconductor process. The proposed structure can perform multiple tunable functionalities by appropriately biasing the integrated circuit: Perfect absorption for a wide range of incidence angles of both linear polarization states, accommodating a spectral range in the vicinity of 5 GHz, with potential also for wavefront control, exemplified via anomalous reflection and polarization conversion. The end vision is for such a design to be scalable and deployable as a practical HyperSurface, i.e., an intelligent multi-functional metasurface capable of concurrent reconfigurable functionalities: absorption, beam steering, polarization conversion, wavefront shaping, holography, and sensing. Copyright © 2020, The Authors. All rights reserved.

关键词： Wavefronts

The Consistent Cuckoo Filter

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The Consistent Cuckoo Filter

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lailong Luo Deke Guo Ori Rottenstreich Richard T.B Ma Xueshan Luo Bangbang Ren Science and Technology on Information System Engineering Laboratory National University of Defence Technology Computer Science and the Department of Electrical Engineering Technion School of Computing National University of Singapore

The emergence of large-scale dynamic sets in networking applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional k candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

关键词： Data structures Elasticity Probabilistic logic Hash functions Merging Indexes Reliability

Time-dependent performance analysis of the 802.11p-based platooning communications under disturbance

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arXiv 2020年

作者： Wu, Qiong Ge, Hongmei Fan, Pingyi Wang, Jiangzhou Fan, Qiang Li, Zhengquan School of Internet of Things Engineering Jiangnan University Wuxi214122 China State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons. However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error is propagated or even amplified downstream along the platoon, i.e., platoon string instability. In this case, the connectivity among vehicles is dynamic, resulting in the performance of 802.11p in terms of packet delay and packet delivery ratio being time-varying. The effect of the string instability would be further deteriorated once the time-varying performance of 802.11p cannot satisfy the basic communication requirement. Unlike the existing works which only analyze the steady performance of 802.11p in vehicular networks, we will focus on the impact of disturbance and construct models to analyze the time-dependent performance of 802.11p-based platooning communications. The effectiveness of the models is validated through simulation results. Moreover, the time-dependent performance of 802.11p Copyright © 2020, The Authors. All rights reserved.

关键词： Military applications