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Development of a Multi-hop Network Using XBee3 Micro-modules for Different Indoor Scenarios: Autonomous Parameter Setting and Signal Monitoring

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SN computer Science 2024年第1期5卷 34页

作者： Hirunkitrangsri, Puwasit Sasiwat, Yoschanin Booranawong, Apidet Department of Electrical and Biomedical Engineering Faculty of Engineering Prince of Songkla University Songkhla 90110 Thailand

In multi-hop wireless sensor networks (WSNs), since the coverage area is larger than the radio range of a single node, relay nodes in the network are employed to transfer the data to the destination with hop-by-hop communication. Here, autonomous and effective multi-hop communication to satisfy wireless communication reliability is significantly required. In this work, the development and the experimental evaluation of a 2.4 GHz indoor multi-hop WSN system are presented. The contributions and novelties of this work are that, first, we develop a multi-hop WSN utilizing IEEE 802.15.4 Xbee3 micro-modules, and we build the communication protocol for all wireless sensor nodes, as well as the graphical user interface (GUI) for autonomous parameter setup and signal monitoring. Second, the proposed system is tested in different indoor scenarios, including line-of-sight (LoS) communications, non-line-of-sight (NLoS), different floor communications, and spiral staircase tower scenarios. The effects of different node placement locations, communication directions, and transmission powers are also explored. Experimental results demonstrate that the proposed system can operate autonomously and efficiently in all test scenarios, where a packet delivery ratio (PDR) reaches 100% as the successful rate of packet transmission. Additionally, the end-to-end delay (ETED) from the transmitter to the receiver nodes and the received signal strength indicator (RSSI) level measured from each communication link are also reported for evaluation and analysis. Experimental results indicate our success in implementation and usability for WSN indoor deployment. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 2.4 GHz IEEE 802.15.4 Multi-hop WSN XBee3 micro

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A new method for promoting the detection of breast cancer in thermograms: applying deep autoencoders for eliminating redundancies in parallel with preserving independent components

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4085-4099页

作者： Shojaedini, Seyed Vahab Bahramzadeh, Bahram Biomedical Engineering Department Iranian Research Organization for Science & ampTechnology Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran

Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. Recently, deep learning has emerged as a promising framework for analyzing breast thermograms. However, the performance of deep neural networks heavily relies on the volume of training images, and the limited number of labeled thermograms necessitates the generation of synthetic images for training. This article introduces a novel method that eliminates redundant or destructive information in synthetic thermograms while preserving essential and independent features using deep autoencoder concepts. Consequently, the proposed strategy enhances the representation of synthetic images for training deep networks, leading to improved breast cancer detection in thermograms. Evaluating the performance of the proposed method on the DMR-IR dataset shows a significant improvement in detecting cancerous breasts in thermograms compared to benchmark methods. Integrating this innovation into the basic model achieved an average accuracy of 92.3%, sensitivity of 93%, and specificity of 91.4%, surpassing the basic model’s 89.1%, 86%, and 92.5%, respectively. The proposed method demonstrated superior performance in mitigating overfitting, as evidenced by a smaller gap between training and validation curves, resulting in a 7% improvement in accuracy and a 3.2% increase in sensitivity. Although there was a slight decrease of 1.1% in specificity, it was outweighed by other parameter improvements. Additionally, the method led to a significant reduction in the dispersion range of test results, approximately six times less than alternative approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lung cancer

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CTRL: Clustering Training Losses for Label Error Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4121-4135页

作者： Yue, Chang Jha, Niraj K. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States

In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting the label errors can significantly increase their efficacy. We propose a novel framework, called CTRL11CTRL is open-source: https://***/chang-yue/ctrl. (Clustering TRaining Losses for label error detection), to detect label errors in multiclass datasets. It detects label errors in two steps based on the observation that models learn clean and noisy labels in different ways. First, we train a neural network (NN) using the noisy training dataset and obtain the loss curve for each sample. Then, we apply clustering algorithms to the training losses to group samples into two categories: cleanly labeled and noisily labeled. After label error detection, we remove samples with noisy labels and retrain the model. Our experimental results demonstrate state-of-the-art error detection accuracy on both image and tabular datasets under labeling noise. We also use a theoretical analysis to provide insights into why CTRL performs so well. © 2020 IEEE.

关键词： Neural networks

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A New Approach to Determine Feasible Operating Region of Unbalanced Distribution Networks with Distributed Photovoltaics

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IEEE Transactions on Power Delivery 2025年第3期40卷 1493-1504页

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distribution network. The feasible operating region of an active distribution network is constituted by its range of active-reactive power exchanges with the bulk power system without breaching network constraints. This study explores the potential of highly penetrated single- and three-phase distributed photovoltaics and voltage-sensitive loads to determine the feasible operating region at different operating points. The cumulative response of Volt-Var controlled distributed photovoltaics and voltage-sensitive loads determines the power flow at the bulk power system-active distribution network interconnection, ensuring feasibility with respect to voltage magnitude and unbalance constraints. Further, the correspondence of feasible operating region with the tap-settings of substation transformer enables a novel feature to traverse throughout the available operating region and facilitate bulk power system-active distribution network power coordination. Additionally, a nodal sensitivity-based adjustable Volt-Var control scheme is proposed for distributed photovoltaics, improving the feasible operating region. The proposed strategy is validated on bulk power systemactive distribution network integrated test system. Bulk power system: IEEE-9 bus system-Active distribution network: (highly unbalanced IEEE-13 bus distribution system (medium voltage) + modified CIGRE low voltage system). © 1986-2012 IEEE.

关键词： Power distribution networks

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Graph isomorphism—Characterization and efficient algorithms

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High-Confidence Computing 2024年第4期4卷 48-55页

作者： Jian Ren Tongtong Li Department of Electrical and Computer Engineering Michigan State UniversityEast Lansing 48824USA

The Graph isomorphism problem involves determining whether two graphs are isomorphic and the computational complexity required for this *** general,the problem is not known to be solvable in polynomial time,nor to be *** this paper,by analyzing the algebraic properties of the adjacency matrices of the undirected graph,we first established the connection between graph isomorphism and matrix row and column interchanging ***,we prove that for undirected graphs,the complexity in determining whether two graphs are isomorphic is at most O(n^(3)).

关键词： Undirected graph Characterization Isomorphism Algorithm Polynomial time complexity

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A novel trusted hardware-based scalable security framework for IoT edge devices

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Discover Internet of Things 2024年第1期4卷 4页

作者： Khan, Mohd Hatami, Mohsen Zhao, Wenfeng Chen, Yu Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA. © The Author(s) 2024.

关键词： Internet of things

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Monitoring and Classification of Human Sleep Postures, Seizures, and Falls From Bed Using Three-Axis Acceleration Signals and Machine Learning

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SN computer Science 2024年第1期5卷 104页

作者： Intongkum, Chawakorn Sasiwat, Yoschanin Sengchuai, Kiattisak Booranawong, Apidet Phukpattaranont, Pornchai Department of Electrical and Biomedical Engineering Faculty of Engineering Prince of Songkla University Songkhla 90110 Thailand

A system for monitoring and classifying human activity and sleep postures in bed using three-axis acceleration signals is presented in this paper. In this low-cost system, a three-axis accelerometer sensor using a single GY-521 sensor is placed on the abdominal muscles of the human body to measure human activity and sleep posture signals. The sensor is connected and communicates with the Arduino Mega for processing. Focused activities and sleep postures in bed, including (a) sleeping on his back, (b) turning around to sleep on his side, (c) sleeping on his side, (d) turning around and falling from the bed, (e) lying on the ground, and (f) seizure sleeping, are tested and evaluated. Finally, signal feature extraction using thirty-five features from three groups of calculations and classification using a K-nearest neighbors (KNN) algorithm are applied. Experiments are conducted in a laboratory, and results indicate that the proposed system could automatically monitor different human postures in bed and falls in real-time. Acceleration signals measured from activities and sleep postures have their own patterns and characteristics. Additionally, the average classification accuracy using the best four features obtained 93% for the postures a) to f) of 97.5%, 73.9%, 98.4%, 88.7%, 98.4%, and 91.1%, respectively. Here, before falls, falls, and seizures can be accurately detected. Our system and results can thus be used to support caretakers, physicians, and medical staff in the evaluation, planning, and treatment of the elderly and patients in healthcare systems. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 3-Axis accelerometer Classification Falls Monitoring Sleep postures

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Quantum Circuits for Stabilizer Error Correcting Codes: A Tutorial

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IEEE Circuits and Systems Magazine 2024年第1期24卷 33-51页

作者： Mondal, Arijit Parhi, Keshab K. University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. Along with the exploration into novel and more efficient quantum error correction codes, it is also essential to design circuits for practical realization of these codes. This article serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We first describe Shor's 9-qubit code which was the first quantum error correcting code. We discuss the stabilizer formalism along with the design of encoding and decoding circuits for stabilizer codes such as the five-qubit code and Steane code. We also design nearest neighbor compliant circuits for the above codes. The circuits were simulated and verified using IBM Qiskit. © 2001-2012 IEEE.

关键词： Timing circuits

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Physical-Layer Identification of Wireless IoT Nodes Through PUF-Controlled Transmitter Spectral Regrowth

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IEEE Transactions on Microwave Theory and Techniques 2024年第2期72卷 1045-1055页

作者： Zhou, Qiang He, Yan Yang, Kaiyuan Chi, Taiyun Rice University Department of Electrical and Computer Engineering HoustonTX77005 United States

Securing low-power Internet-of-Things (IoT) sensor nodes is a critical challenge for the widespread adoption of IoT technology due to their limited energy, computation, and storage resources. As an alternative to the traditional wireless security solution based on cryptography, there has been growing interest in RF physical-layer security, which promises a lower overhead and energy cost. In this work, we demonstrate energy-efficient physical-layer identification, a.k.a., RF fingerprinting, designed specifically for resource-constrained IoT nodes. To enhance the identification performance beyond prior demonstrations using off-the-shelf radios, we propose a minor modification to the radio frontend by integrating a digital physically unclonable function (PUF). The PUF controls the transmitter (TX) spectral regrowth as the RF fingerprint (RFF), enhancing its uniqueness and identification space beyond solely relying on transistor intrinsic process variations. As a proof of concept, a 2.4-GHz physical-layer identification is implemented in the GlobalFoundries 45-nm CMOS SOI process. It achieves 4.7-dBm output power and 36% efficiency, which are comparable to state-of-the-art low-power 2.4-GHz power amplifiers (PAs). Additionally, it demonstrates significant improvement in RFF reliability, uniqueness, and identification space over prior physical-layer identification demonstrations. The identification rate and security performance of the proposed approach under different attack models are also discussed. © 1963-2012 IEEE.

关键词： Power amplifiers

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Image description using tags latent concepts in convolutional neural networks

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Multimedia Tools and Applications 2024年 1-24页

作者： Barati, Alireza Farsi, Hassan Mohamadzadeh, Sajad Department of Electrical and Computer Engineering University of Birjand Shahid Avini Highway Birjand Iran

Automatic image annotation systems are employed to describe the visual content of images via tag assignment. Most of these image description systems use deep convolutional neural networks as feature extractors or multi-label classification systems. However, the majority of these systems do not optimally employ latent variables and image concepts within the side information. The idea of this study is to add a latent variable to the CNN-based annotation problem. A Bayesian modeling framework is used for this purpose. High-Level Semantic Concepts of tags are used as an instance of a latent variable to implement the proposed model. The Bayesian modeling result interpretation leads to localizing the entire image annotation process and breaks it down into smaller problems. Thus, a CNN-based sub-network is trained independently for each sub-problem and converted to an expert in annotation for a particular high-level semantic concept. To estimate tags in the test phase, the tags obtained from all sub-networks are combined in weighted form. The weight of each sub-net is proportional to the relation of its high-level semantic concept with the test image content. These weights are obtained from a separate general CNN as a classifier of concept. At the end, a tag refinement method is used to remove noisy tags. The obtained results (F1 values are 40.3, 35.2, and 38.1 for Corel5k, ESPGame, and IAPRTC12 datasets, respectively) confirm the effectiveness of the proposed method and can be comparable with the state-of-the-art annotation systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image annotation

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