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7th International Conference on Intelligent, Interactive systems and Applications, IISA2022

作者： Min, Xiao Mei, Guo Weiping, Zhu School of Computer and Artificial Intelligence XiangNan University Hunan Chenzhou423000 China Advanced Embedded Computing and Intelligent Medical Systems Hunan Engineering Research Center Hunan Chenzhou423000 China

With the development of science and technology, the computer age has *** a new type of computer network application technology, cloud computing technology is of great significance to promote China's economic ***, in the development of cloud technology, it faces serious security *** cloud providers all claim that their services are very secure in all aspects, especially in data ***, for large enterprises, business-related data is their *** enterprises will not put their important applications on the public cloud for operation, and private cloud has great advantages in this *** paper introduces the relevant concepts and network security deployment methods of cloud technology and private cloud security *** research on private cloud security platform is to better resist the next generation of security *** network security on private cloud security platform can reduce the informatization cost of enterprises, public institutions and government departments and improve work efficiency. © 2022 The Authors. Published by Elsevier B.V.

关键词： Network security

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Design and Implementation of Home Medicine Box AI System

Design and Implementation of Home Medicine Box AI System

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Xia Zhang Wenfen Zhang College of Computer and Artificial Intelligence Xiangnan University Chenzhou China The Hunan Engineering Researcher Center of Advanced Embedded Computing and Intelligent Medical Systems Chenzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

In order to solve the problems of daily medication and health and safety care for the elderly, this paper uses the Internet of Things technology to design and implement a home medicine box AI system based on Raspberry PI, which is composed of three parts: medicine cabinet management system, electronic bracelet system and wechat mini program. The medicine cabinet control system utilizes Raspberry PI 4b as the central processing unit, incorporating a timing sensor, voice sensor, and temperature and humidity sensor. The electronic bracelet's control system utilizes STM32 as the primary control board, while the development board incorporates a dual-mode positioning sensor, cardiovascular oxygen sensor, voice module, network module, and six-axis acceleration sensor interconnected with dupont wires. The electronic bracelet control system facilitates functions such as positioning, heart rate acquisition, voice broadcasting, wireless communication, and attitude acquisition. The mobile WeChat mini program enables remote monitoring and control of medicine cabinets and the health status of elderly individuals, providing functions such as temperature and humidity monitoring, timed voice medication reminders, heart rate monitoring, posture monitoring, and positioning. The test results demonstrate the system's stability and reliability, thereby providing robust technical support for home monitoring.

关键词： Temperature sensors Temperature measurement Heart rate Social networking (online) Humidity Message services Control systems Real-time systems Older adults Monitoring

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Destination Scheduling for Secure Pinhole-Based Power-Line Communication

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2023年 4卷 2245-2260页

作者： Kundu, Chinmoy Dubey, Ankit Tonello, Andrea M. Nallanathan, Arumugam Flanagan, Mark F. University College Dublin School of Electrical and Electronic Engineering Dublin 4D04 V1W8 Ireland Indian Institute of Technology Jammu Department of Ee Jammu181221 India Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt9020 Austria Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved. © 2020 IEEE.

关键词： Impulse noise

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Improving Fault Tolerance of LoRaWAN With Predicting Packet Collision

Improving Fault Tolerance of LoRaWAN With Predicting Packet ...

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International CSI computer Conference, CSICC

作者： Reza Rezazadeh Yasser Sedaahat Ismail Ghodsollahee Farzad Azizi Department of Computer Engineering Dependable Distributed Embedded Systems (DDEms) Laboratory Ferdowsi University of Mashhad Mashhad Iran Department of Computer Engineering Amirkabir University of Technology Tehran Iran

the dependability of IIoT functionality is strongly influenced by its communication protocols. One of the most popular communication standards for the Internet of Things, whose features make it attractive in the IIoT field, is LoRaWAN. LoRaWAN can be considered a low-consumption competitor to cellular networks. This protocol has important features such as long-range coverage, low energy consumption, and a low implementation cost. Although the LoRaWAnfeatures make it a good choice for IoT applications, the existence of real-time constraints and a harsh environment in the IIoT field make it a challenge for network designers to guarantee the dependability of IIoT networks with LoRaWAN. Therefore, in this paper, to overcome this challenge, a novel method is presented for improving energy consumption efficiency, latency, and fault tolerance of the LoRaWAN protocol. The evaluation results show that the proposed method improves packet delivery and latency rate by 11% and 21% compared to standard LoRaWAn.

关键词： Energy consumption Fault tolerance Protocols Costs Fault tolerant systems Packet loss Real-time systems

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Trikarenos: Design and Experimental Characterization of a Fault-Tolerant 28nm RISC-V-based SoC

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IEEE Transactions on Nuclear Science 2025年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gurkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca Integrated Systems Laboratory ETH Zurich Switzerland Computer Architecture for Embedded Systems University of Twente Netherlands Department of Industrial Engineering University of Trento Italy Delft University of Technology Department of Space Engineering Netherlands University of Rome Department of Electronic Engineering Tor Vergata Italy Information Engineering at the University of Bologna Department of Electrical Electronic Italy

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, a SoC implemented in TSMC 28nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECC) protected memory are corrected by a scrubber, showing an estimated cross-section per bit of up to 1.09 × 10−14 cm2 bit−1. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross-section up to 3.23 × 10−11 cm2, with the remaining uncorrectable vulnerability below 5.36 × 10−12 cm2. When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10% of injections into the SoC produced correct results, while 100% of injections in the TCLS-protected cores were handled *** 12.28%of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop cross-section of up to 1.28 × 10−14 cm2/FF. © 1963-2012 IEEE.

关键词： Radiation damage

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Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants

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Journal of Big Data 2025年第1期12卷 1-30页

作者： Ayyad, Sarah M. Sallam, Nada M. Gamel, Samah A. Ali, Zainab H. Computers and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Mansoura Egypt Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia Faculty of Engineering Horus University Damietta Egypt Department of Embedded Network Systems and Technology Faculty of Artificial Intelligence Kafrelsheikh University Kafr El-Sheikh Egypt Department of Electronics and Computer Engineering School of Engineering and Applied Sciences at Nile University Giza Governorate Egypt

Identification and precise classification of plants are crucial in improving plant quality and economic viability, particularly in an industrial setting. In a faster-growing world, there is a growing demand for fully automated tomato detection and grading systems. Within the past few years, applying deep learning in detecting and classifying tomatoes into different classes has gained popularity. This study aims to build a new framework for the efficient automated harvesting of tomato plants based on deep learning. The new model integrates the capabilities of Particle Swarm Optimization (PSO) and the You Only Look Once version-8 (YOLOv8) architecture for better hyperparameter optimization and improved performance results. For the first time, it classifies and detects ripe, semi-ripe, and unripe tomatoes, in addition to diseased and rotten tomatoes. To validate the efficacy of the proposed YOLO-v8 network’s performance, three experiments were conducted employing a unique dataset, collected from different sources. Firstly, two experiments were conducted to formally confirm whether or not the utilized data augmentation technique significantly improved, one with data augmentation and another one with the end-to-end framework without data augmentation. Secondly, the proposed YOLOv8 was compared with other YOLO versions, e.g., YOLOv3, YOLOv5, S-YOLOv5, YOLOv7, and YOLOv8. Thirdly, the proposed framework was compared with many cutting-edge object detection architectures for tomato harvesting, e.g., Convolutional neural network (CNN), Mask R-CNN and color analysis, and other models based on handcrafted features. The enhancements made to the original YOLO-v8 network have attained promising results. The experiments on the collection of different datasets reveal that the proposed model performs with the highest precision, recall, F1-score, and mean average precision (mAP) of 0.89, 0.9, 0.89, and 0.89 (mAP@0.5:0.95), respectively, exceeding other models. This framework offers a

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RETRACTED ARTICLE: Terahertz fading model for wireless nanosensor networks in advanced medical manufacturing technologies

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International Journal of Advanced Manufacturing Technology 2024年第9-10期132卷 5175-5175页

作者： Javed, Ibrahim Tariq Qureshi, Kashif Naseer Alharbi, Fares Jeon, Gwanggil Department of Computer Science Bahria University Islamabad44000 Pakistan Computer Science Department Shaqra University Shaqra15526 Saudi Arabia School of Electronic Engineering Xidian University Xi’an710071 China Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Nanomaterials such as graphene have allowed the manufacturing of different types of nanosensors. These nanosensors will sense and stimulate action, store, and process the data and transmit electromagnetic signals in the terahertz range. Enabling communication will allow nanosensors to form a network that will lead to various applications in different fields, especially in advanced manufacturing technologies and biomedical. However, the signal propagated by nanoantennas in terahertz gets attenuated by molecules present in the medium, such as the human body. This paper presents a simple fading model for wireless nanosensor networks for advanced manufacturing technologies and applications that incorporates molecular absorption and molecular noise. The model estimates the log-distance path loss model with random attenuation caused by the molecules present in the medium. It has been observed that the water molecule present inside the human body produces significant attenuation to terahertz frequencies. The path loss model presented has been used to identify certain frequency windows within the terahertz band that can be used for communication purposes in nanoapplications. For instance, a 400-GHz wideband from 100 to 500 GHz has been identified with a mean attenuation of 0.021 dB/cm inside the human body. Furthermore, channel capacity is computed for these bands that shows the specified bands achieve better throughput than those with more significant molecular attenuation. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Graphene

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A Clustering-BIST Design for Inter-Layer Vias in 3D ICs Based on Walking Pattern Approach

TechRxiv

引用

TechRxiv 2023年

作者： Menbari, Ahmad Rahimi, Hemin Jahanirad, Hadi Ziener, Daniel The Department of Electrical Engineering University of Kurdistan Sanandaj Iran The Department of Computer Architecture and Embedded Systems Ilmenau University of Technology Ilmenau Germany

In the realm of 3D monolithic integrated circuits, inter-layer vias are prone to defects during fabrication, assembly, and operation that necessitates robust Built-In Self-Test solutions. This paper introduces an innovative BIST design aimed at the detection and localization of different types of faults in irregularly placed ILVs using a walking pattern methodology. In the proposed BIST methodology, the ILVs are clustered based on the fault occurrence probability distribution. Then, an effective detection approach is developed for all stuck-at faults, bridging faults, and almost all multiple faults in a single cluster. This approach allows designers to adjust different parameters such as fault coverage percentage, fault localization, and test time in alignment with specific requisites. The proposed BIST method reduces test configurations to two and test time to just 20 cycles by dividing ILVs into more clusters, overcoming a key limitation of recent approach. This methodology also demonstrates improved efficiency in terms of power consumption, area, and hardware utilization, particularly for large-scale benchmarks. For instance, when considering LU32PEENG and the scenario where ILVs are divided into 64 clusters, the power, area, and hardware overhead are 0.82% and 1.03%, and 1.14%, respectively. © 2023, CC BY.

关键词： Built-in self test

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Perception of 3D Scene Based on Depth Estimation and Point-Cloud Generation 14th

Perception of 3D Scene Based on Depth Estimation and Point-C...

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14th International KES Conference on Intelligent Decision Technologies, KES-IDT 2022

作者： Saleh, Shadi Manoharan, Shanmugapriyan Nine, Julkar Hardt, Wolfram Department of Computer Engineering Chemnitz University of Technology Straße der Nationen 62 Chemnitz09111 Germany Embedded Systems Chemnitz University of Technology Straße der Nationen 62 Chemnitz09111 Germany

ISBN: (纸本)9789811934438

Depth estimation and object instance recognition capabilities in 3D space are critical for autonomous navigation, localization, mapping, and robotic object manipulation. RGB-D images and LiDAR (Light Detection and Ranging) point clouds are the most descriptive formats for depth information. However, these depth sensors have many shortcomings, such as low effective spatial resolution and capturing a scene from a single perspective. Our work is mainly focused on reproducing a denser and more comprehensive 3D scene structure of given monocular RGB images employing depth and 3D object detection. The first contribution is the proposal of two architectures for estimating depth maps based on an unsupervised learning system, which are utilized to analyze the structure from motion and 3D geometric constraints and deliver better results than existing methodologies. The second contribution involved the generation of a 3D point cloud based on the estimated depth map and a monocular RGB frame. The primary goal of this part was to enhance the visualization of the 3D scene structure. Our proposed solutions achieve better outcomes in terms of ARD, RMSE, RMSE (log), accuracy and other evaluation metrics with lower but equivalent results compared to state-of-the-art methods with a maximum depth of 80 m. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Digital Learning Ecosystem: Online Concept for engineering Education

Digital Learning Ecosystem: Online Concept for Engineering E...

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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Anatolijs Zabasta Volodymyr Kazymyr Nadezhda Kunicina Oleksandr Velihorskyi Faculty of Computer Science Information Technology and Energy Riga Technical University Riga Latvia Information and Computer Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine Radio Engineering and Embedded Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and Simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the Technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts.

关键词： Training Surveys Remote laboratories Biological system modeling Ecosystems Hardware Stakeholders engineering education Kernel Programming profession

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