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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Cui, Hongzhen Wang, Shichao Zhu, Xiaoyue Peng, Yunfeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Electronics and Information Engineering Liaoning University of Technology Liaoning China

ISBN: (纸本)9798350376548

Segmentation of the right ventricle (RV) holds significant clinical implications for the assessment of cardiac function in conditions such as pulmonary hypertension. Precise delineation of the right ventricle from cardiac magnetic resonance images (CMRI) is crucial in the diagnosis of various cardiac conditions. It provides clinicians with essential information for patient evaluation, treatment planning, and subsequent rehabilitation strategies. Currently, the development of fully automated segmentation models for the right ventricle heavily relies on deep learning techniques, necessitating meticulous manual annotation of CMRI data to achieve optimal segmentation results. However, the labor-intensive nature of labeling CMRI data requires significant medical expertise and consumes considerable time and resources. In response to these challenges, this study proposes a semi-supervised multi-scale segmentation approach for the right ventricle in CMRI. The method leverages unlabeled data to enhance segmentation accuracy while minimizing the reliance on annotated data. Experimental findings demonstrate the effectiveness of the proposed method in accurately delineating the right ventricle from CMRI datasets. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Design and Analysis of SAR-ADC for Low Power Circuits

Design and Analysis of SAR-ADC for Low Power Circuits

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2024 IEEE International Conference on Information technology, electronics and Intelligent communication Systems, ICITEICS 2024

作者： Kalaiselvi, A. Giftlin Devapriya, K. Gokulprasath, K. Premkumaar, M. Shanthi, K.G. Dhivya Praba, R. Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350382693

This paper introduces an innovative concept centered on developing an 8-bit Asynchronous Successive Approximation Register (SAR) Analog to Digital Converter (ADC) utilizing state-of-the-art 18nm FinFET technology. This advanced asynchronous SAR system encompasses multiple crucial components, including a precisely engineered internal clock generator, a high-performance bootstrapped sample and hold circuit, a sturdy and highly effective dynamic comparator, a precise Digital-to-Analog Converter (DAC), and a sophisticated SAR logic. A notable feature of our design is its exceptionally low power consumption, operating within the microwatt (uW) range. These attributes position our ADC as an appealing solution for applications requiring a delicate balance between power efficiency, moderate resolution, and swift performance. These applications vary widely, from cutting-edge biomedical sensors to memory cores tailored to the high-performance demands of AI-driven computing applications. Essentially, our project not only signifies a technological advancement in SAR ADC design but also holds significant potential for influencing the future landscape of various electronic devices and systems. © 2024 IEEE.

关键词： FinFET

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Block chain and Deep Learning Based Secure communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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Intelligent Prediction of Cardiac Abnormality 4th

Intelligent Prediction of Cardiac Abnormality

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Kiruthika, V. Natesan, Muthu Sathish, E. Vimalarani, G. School of Electronics Engineering Vellore Institute of Technology Chennai600127 India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai603103 India

ISBN: (纸本)9789819994410

Cardiac abnormalities are very common in today’s world. Efficient and early prediction will help in saving the life of several individuals. ECG is a standard technique for prediction of any kind of abnormality prevailing in the heart. An intelligent cardiac abnormality prediction system is developed in this study. The study utilizes the real-time ECG data collected from a hospital. The contributing attributes are selected. The five chosen attributes include QRS value, heart rate, P-axis, R–axis, and T-axis. Machine learning techniques are explored for developing the system. Performance metrics are computed for each classifier and best performing classifier is selected for deploying the model. Naive Bayes has performed well by giving 93.50% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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Detection of Voice Disorder Using Spectral and Statistical Audio Features and SVM-RBF

Detection of Voice Disorder Using Spectral and Statistical A...

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2024 IEEE International Conference on Signal Processing, Information, communication and Systems, SPICSCON 2024

作者： Sultana, Shafrin Aowlad Hossain, A.B.M. Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (纸本)9798331510213

The detection of voice disorders using voice signal processing and machine learning has emerged as a promising early diagnosis and treatment approach. This paper presents a machine learning-based automatic voice disorder detection framework extracting and selecting optimum features from voice audio signals. The practical challenge of class-imbalanced scenario of disorder and healthy voices data is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Several features, i.e., Mel-Frequency Cepstral Coefficients (MFCC), Chroma Short-Time Fourier Transform (STFT), zero crossing rate, root mean square, skewness, and kurtosis;have been studied in spectral and statistical domains. Finally, the optimum features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) technique to reduce the classifier model’s complexity. A support vector machine (SVM) classifier with a Radial basis function kernel is developed, prioritizing simplicity and efficiency. The SVM model is trained and tested using the well-accepted VOICED database of PhysioNet Data Bank where the audio is captured using a smartphone. The performance of the overall framework shows an accuracy of 95% and 100% precision and specificity, demonstrating its resilience for precise evaluations and smartphone-based diagnosis applications. © 2024 IEEE.

关键词： Smartphones

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Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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Intelligent Automation & Soft Computing 2023年第1期35卷 957-969页

作者： T.Shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

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Three-Dimensional Reconstruction Method Based on Infrared Deflectometry

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Photonic Sensors 2025年第2期15卷 63-69页

作者： Ping XIONG Jing ZHAO Jinping CHEN Hao HU Jiangping CAO School of Materials and Environmental Engineering Chengdu Technological UniversityChengdu 611730China School of Network&Communication Engineering Chengdu Technological UniversityChengdu 611730China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China National Institute of Measurement and Testing Technology Chengdu 610021China

Deflectometry is a non-contact and rapid detection method with high sensitivity,which can be utilized in the areas of three-dimensional(3D)reconstruction,surface quality detection,and defect *** has important applications in the automotive industry,optical parts manufacturing,and other *** deflectometry methods require the integration of slope data to reconstruct the absolute surface ***,the integration procedure is error-prone due to the accumulation of random errors into large shape deviations,resulting in inaccurate measurement *** this paper,we extract the position of the centerline of the bright fringe from the stripe image and find the center of the fringe line point by *** the principle of triangulation,the 3D data for the object’s shaped surface can be *** addition,infrared light enhances specular reflection,resulting in better deflectometry performance than that of visible *** show that the proposed method is sensitive to the changes in height and can roughly restore the surface topography of the object without calibration.

关键词： 3D reconstruction infrared deflectometry stripe detection

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Innovative Slot-Enhanced Coplanar Waveguide Fed Circular Patch Ultra-Wideband Antenna for Medical Application

Innovative Slot-Enhanced Coplanar Waveguide Fed Circular Pat...

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2024 International Conference on Smart electronics and communication Systems, ISENSE 2024

作者： Prasad, Aparna Akesha Sanal, S.S. George, Ann Treasa Ramesh, Aishwarya Lakshmanan, Reshma Krishnan, Rahul Adi Shankara Institute of Engineering and Technology Dept of Electronics and Communication Engineering Kalady India SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai Vadapalani India

ISBN: (纸本)9798331528126

The proposed antenna, designed with slots on a circular patch, utilizes a Coplanar Waveguide (CPW) feeding technique. This Ultra-Wideband (UWB) antenna operates effectively across the 3.1 to 10.6 GHz frequency range, achieving an S11 2, the antenna uses a substrate material with a relative permittivity of 4.4 and a thickness of 1.6 mm. The inclusion of slots in the circular patch design improves impedance matching and results in an omnidirectional radiation pattern, which significantly enhances the antenna's performance. This makes it particularly well-suited for biomedical applications. In addition to its broad operational bandwidth and compact size, the proposed antenna demonstrates favorable radiation properties, maintaining a stable pattern across its entire frequency range. © 2024 IEEE.

关键词： Coplanar waveguides

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Application Of Lightweight Facial Expression Recognition Based On Deep Convolutional Asymmetric Residual Attention Network Model In Education Management

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2025年第11期28卷 2227-2237页

作者： Kong, Po School of Electronics and Electrical Engineering Zhengzhou University of Science and Technology Zhengzhou450064 China

In view of the low accuracy of facial expression recognition in natural state, which is easily affected by noise and other factors, this paper proposes a lightweight facial expression recognition method based on deep convolutional asymmetric residual attention network model. In this paper, Ghost module and depth-separable convolution are introduced to replace 1×1 convolution and 3×3 convolution in Bottleneck, which retains more original feature information and improve the feature extraction ability of backbone branches. A parallel deep convolutional residual structure is designed in the shallow network to enhance the model’s ability to represent the local details of facial expressions and integrate them with the global features. A spatial grouping enhanced attention mechanism is established in the deep network to improve the stability of expression feature distribution and enhance the ability of the model to discriminate subtle changes in expression. In order to avoid over-fitting of the model, the output structure of the backbone network is improved without greatly increasing the computational complexity. The Mish activation function is used to replace the ReLU activation function in Bottleneck, which improves the accuracy of expression recognition. The expression recognition accuracy of the proposed method on the published seven-classification dataset RAF-DB, AffectNet-7 and eight-classification dataset AffectNet-8 reaches 88.44%, 63.20% and 60.23%, respectively. The experimental results show that the proposed method can improve the expression recognition accuracy while reducing network parameters. It is proved that the method is effective and has a certain application prospect. © The Author(’s).

关键词： Convolution

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High isolated two element MIMO antenna with novel isolator for WiFi, WLAN and wireless applications

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International Journal of Information technology (Singapore) 2024年第1期16卷 517-524页

作者： Mishra, Brijesh Siddique, Shadab Azam Singh, Rakesh Kumar Chandra Bhan Tripathi, Ram Racksha Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Bengaluru India Supaul College of Engineering Bihar Supaul India Shambhunath Institute of Engineering and Technology U.P Prayagraj India

This paper proposes a two-element multiple input multiple output (MIMO) antenna (0.37 λd× 0.57 λd mm 2) with high isolation for wireless fidelity (WiFi) and wireless applications. The antenna is designed with a... 详细信息

This paper proposes a two-element multiple input multiple output (MIMO) antenna (0.37 λ_d× 0.57 λ_d mm ²) with high isolation for wireless fidelity (WiFi) and wireless applications. The antenna is designed with a new isolator and symmetrical radiating elements to achieve resonance at 2.3 and 2.8 GHz in a wideband of 2.1–3.2 GHz. The antenna exhibits a fractional bandwidth and maximum isolation of 41.5% and - 44.6 dB, respectively, and maximum gains of 2.9 and 4 dB at 2.3 and 2.8 GHz. The antenna also demonstrates 78% radiation efficiency and a steady radiation pattern across the whole resonating band. MIMO properties, such as envelope correlation coefficient (ECC) and total active reflection coefficient (TARC), are found to be within acceptable ranges. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： ECC and TARC Isolator MIMO antenna Wideband WiFi

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