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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8138 条记录，以下是461-470 订阅

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Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System 5

Design and Evaluation of Generative Adversarial Network for ...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Vidyalakshmi, R. Gayathri, N. Aarthi, M.S. Kathirvel, N. Bhuvaneshvari, K. Sivaramkrishnan, M. Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India P.A. College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331504403

For the preservation of historical materials and the enhancement of visual representation, image colorization is crucial. Image colorization adds color to black-and-white or grayscale images, revitalizing old photographs and papers. It affords us a more immersive and relatable experience of history. Additionally, colorization imparts realism and liveliness to visual content. It facilitates audience engagement, emotional expression, and the development of visually captivating content that elicits a response and imparts a memory. the process of image colorization utilizing Deep Learning (DL) models is investigated in this study. the pre-processing techniques utilized to ensure data integrity and consistency are sourced from Kaggle. the image colorization challenge is built using DL models, notably Generative Adversarial networks (GANs) and Convolutional Autoencoders (CAEs). these algorithms learn to colorize grayscale images through the use of training data. Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) are some of the metrics utilized to assess the models. the evaluation results indicate that the GAN model exhibits superior performance in image colorization compared to the CAE model. this is supported by higher PSNR and SSIM values, as well as lower values for LPIPS and FID. © 2024 IEEE.

关键词： Generative adversarial networks

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Inerrant-Bits Consumption for Over-the-Air Computation-based Multi-tier Computing 5

Inerrant-Bits Consumption for Over-the-Air Computation-based...

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5th international conference on Communications, Information System and Computer Engineering, CISCE 2023

作者： Wan, Junjie Zhang, Fan Wang, Kunlung East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China

ISBN: (纸本)9798350326796

In this paper, we propose an novel performance metric inerrant-bits consumption (IBC) in an Over-the-Air computation (AirComp) based multi-tier computing system to evaluate the computation efficiency, which is based on the total task computation and communication energy consumptions. then we solve an optimal transmit power design problem to minimize the IBC of an AirComp system under the peak power constraint of sensors. To address the non-convexity of the optimization problem, an efficient optimization algorithm is developed to obtain a closed-form solution. Given the power allocation result, we obtain the task offloading result. By comparing our optimal power transmission policy with two benchmark policies in simulation, we show that our proposed scheme outperforms the baseline schemes with different SNR and the sensor number setups. © 2023 IEEE.

关键词： Computational efficiency

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An Automated Face Mask Detection Using Ensemble Learning Approach 5

An Automated Face Mask Detection Using Ensemble Learning App...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kumar, B. Kiran Mounika, Guggilam Harish, R.V.S. Nagarjuna, Valeti St. Ann's College of Engineering & Technology Embedded Systems Andhra Pradesh Chirala523187 India St. Ann's College of Engineering & Technology Andhra Pradesh Chirala523187 India Kallam Haranadhareddy Institute of Technology Dept of Cse NH-16 Andhra Pradesh Chowdavaram India

ISBN: (纸本)9798350379945

the COVID-19 pandemic and other infectious diseases are spreading quickly, which has brought attention to how essential face masks are in stopping the spread of respiratory disorders. To ensure compliance with mask-wearing rules, automatic face mask detection devices are becoming increasingly important. the application of Automated Convolutional Neural networks (ACNN) in conjunction with Internet of things (IoT) devices for an Automated Face Mask Detection is the main emphasis of this research. the suggested method correctly detects people wearing or not wearing face masks in real-time video and picture data by leveraging ACNN, a deep-learning architecture created for image segmentation. A wide range of labeled photos of people wearing and not wearing masks are used to train the FCNN. the model is ideal for real-world applications in public areas, healthcare facilities, and educational institutions due to its high accuracy and efficiency. Real-time monitoring and notifications are made possible by the integration of IoT devices, which improves the operation of the system. In order to collect images and videos data, IoT sensors and cameras are positioned strategically in specific regions. A central processing unit (CPU) that executes the trained FCNN model is connected to these devices. Using email, SMS, or a mobile app, the system instantly notifies the appropriate authorities or persons when it finds someone not donning a face mask. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review of sensor System Schemes for Integrated Navigation 5

A Review of Sensor System Schemes for Integrated Navigation

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5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Iqbal, Umar Abosekeen, Ashraf Elsheikh, Mohamed Noureldin, Aboelmagd Korenberg, Michael J. Mississippi State Univ Elect & Comp Engn Mississippi State MS 39762 USA Mil Tech Coll Elect & Comp Engn Cairo Egypt Univ Calgary Dept Geomat Engn Calgary AB Canada Queens Univ Royal Mil Coll Canada Elect & Comp Engn Kingston ON Canada Queens Univ Elect & Comp Engn Kingston ON Canada

ISBN: (纸本)9781665482370

GNSS navigation requires an unobstructed line-of-sight view of four or more satellites with suitable geometry to compute latitude, longitude, altitude, and time. GNSS signal weakens in degraded environments such as Urban Canyons, Tunnels, Under Passes, and Green Tunnels. therefore, GNSS alone cannot provide reliable navigation support in challenging environments. To address this limitation, GNSS can be augmented with multiple other navigation sensors to provide an integrated solution, including inertial measurement units, magnetometers, and radars. Low-cost, small size and lightweight MEMS sensors are used for a wide range of navigation applications. However, adding each sensor increases the complexity of the systems as each sensor independently measures a particular parameter. Multi-sensor data fusion techniques, such as Kalman Filter (KF), play a vital role in improving the navigation accuracy of the system. this paper reviews multiple sensor schemes for integrating two accelerometers, a gyroscope, a magnetometer, and Adaptive Cruise Control Radar augmented with GNSS to provide an integrated multi-sensor navigation system. these multiple sensor schemes were tested in an actual road trajectory in Kingston. In addition, GNSS outages were intentionally introduced on this road trajectory to examine the performance of different Schemes for various motion dynamics.

关键词： Land Vehicle Navigation Inertial sensors GNSS Kalman Filter Multiple sensor Schemes

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7th EAI international conference on Robotics and networks, ROSENET 2023

7th EAI International Conference on Robotics and Networks, R...

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7th EAI international conference on Robotics and networks, ROSENET 2023

ISBN: (纸本)9783031644948

the proceedings contain 15 papers. the special focus in this conference is on Robotics and networks. the topics include: KineFormer: Solving the Inverse Modeling Problem of Soft Robots Using Transformers;multiple Node Localization in Cognitive Radio-Based Wireless sensor networks Using Grid Search;systematic Review of Network Slicing Resource Management in 5G;comparatively Studying Modern Optimizers Capability for Fitting Vision Transformers;Comparing LSTM and Transformer for Video Depth Estimation;style Transfer to Enhance Data Augmentation for Facial Action Unit Detection;best Image processing for Higher Face Detection Rate Using Haar Cascades;prediction of Road Traffic Accident Severity Using Machine Learning Techniques in the Case of Addis Ababa;detection of Misinformation Related to Pandemic Diseases Using Machine Learning;Test the Capability of Arduino TinyML for Machine Learning;Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals;Explainable AI for Discovering Disease Biomarkers: A Survey;analytical Approach for Mitigating Phishing Attempts.

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sensor Interfacing with Multicore Aurix TC397 Board 8th

Sensor Interfacing with Multicore Aurix TC397 Board

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8th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Ankad, Sahana Patil, Varsha Bhat, Sahana Channalli, Shriraksha Basawaraj Nissimagoudar, Prabha C. Kle Technological University Karnataka Hubli India

ISBN: (纸本)9789819766772

AURIX Board Multicore Application for Effective Temperature and Humidity sensor Interface. the creation of a multicore application for simultaneous temperature and humidity sensor data processing and acquisition on an AURIX board is presented in this paper. the program takes advantage of the platform’s multicore capabilities to handle data and communicate with sensors efficiently by using distinct cores. Communication and the retrieval of raw data from every sensor are made easier by dedicated drivers. Preprocessing and meaningful unit conversion are applied to acquired data before it is shown in real time over a UART interface. Compared to single-core implementations, this multicore technique clearly increases efficiency and responsiveness, opening the door for future applications integrating a variety of sensor kinds and capabilities. this paper highlights the advantages of parallel processing and offers insightful information on multicore programming for sensor interfacing on the AURIX platform. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Humidity sensors

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Meta-heuristic Artificial Humming Bird Algorithm Based Energy Efficient Cluster Head Selection (MAHA-EECHS) in Wireless sensor networks 5

Meta-heuristic Artificial Humming Bird Algorithm Based Energ...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Kusla, Vipan Brar, Gurbinder Singh Garg, Vikash Kumar Bansal, Ankit Kaushal, Rajesh University Ludhiana Department of Computer Science Ct Punjab India University Ludhiana Department of Cse Ct Punjab India Sliet Department of Computer Science and Engineering Punjab Longowal India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665475242

A wireless sensor network (WSN) improves wireless communication by using hundreds or thousands of nodes to gather data. the lifespan of the nodes and balanced energy consumption are the major issues in the WSN. Long-term WSN efficiency requires optimising node energy. Selecting the optimal node as the cluster head improves energy usage in wireless sensor networks. the Artificial Hummingbird algorithm is used in this paper to identify the best cluster head selection in homogenous wireless sensor networks. the proposed algorithm's innovation lies in the fact that it takes into account a number of parameters like residual energy, intra-cluster distance, and balanced cluster formation while choosing a CH from a homogeneous sensor network. the performance analysis of the proposed algorithm considers four parameters: average energy consumption, total energy consumption, first node death, and residual energy. When compared to other algorithms, MATLAB-based simulation analyses show that the proposed algorithm MAHA-EECHS outperforms them. © 2023 IEEE.

关键词： Base stations

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X-Ray Insights: Comprehensive Dataset for Bone Fracture Detection Across Diverse Anatomical Regions 5

X-Ray Insights: Comprehensive Dataset for Bone Fracture Dete...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bhuria, Ruchika Gupta, Sheifali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331504403

Effective diagnosis and treatment in medical imaging depend on exact identification of bone fractures. this paper presents a fresh approach for bone fracture diagnosis with MobileNetV2, a lightweight and effective convolutional neural network derived from deep learning. Leveraging transfer learning, the model fine-tunes a pre-trained MobileNetV2 architecture using an X-ray picture dataset. the approach consists in several phases: data collecting, pre-processing, model construction, training, evaluation, and application. Resizing X-ray images to 224 × 224 pixels, standardizing pixel values, and using data augmentation methods to improve model resilience are among pre-processing tasks. Custom classification layers-Dense and Dropout layers-along with some layer freezing to preserve pre-trained features-extend the MobileNetV2 model to solve the classification job. Built on binary cross-entropy loss function and Adam optimizer, the model is trained on a well-split dataset including test, validation, and training sets. Evaluation shows that the model is efficient;a classification report reveals exceptional performance criteria. the model achieves precision, recall, and F1-score of 0.95 for both fractured and non-fractured classes, therefore suggesting great accuracy in bone fracture diagnosis. the model's general accuracy is 0.95;macro and weighted averages regularly show performance over all classes. With its great accuracy and efficiency, which improve diagnosis capability and patient outcomes, this MobileNetV2-based model presents a dependable tool for medical professionals that fits very well for clinical. © 2024 IEEE.

关键词： Deep neural networks

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Challenges and Resolutions for Integrating PV Solar Plants into the Grid using AI Techniques in a Uniform Environment 5

Challenges and Resolutions for Integrating PV Solar Plants i...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kant, Surya Singh, Sarabjit Mehra, Komal Vichoray, Chandan Kharayat, Pushpendra Singh Soni, Mukesh Graphic Era Hill University Deptt. of Electronics & Comm. Engg Bhimtal India Rimt University Deptt. of Electrical Engg Punjab Mandi Gobindgarh India Ramdeobaba University School of Management Nagpur India Graphic Era Deemed to be university Department of Electrical Engineering Dehradun248002 India Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Tathawade Pune India

ISBN: (纸本)9798331504403

Advancements in processing power, tools, and data generation have enabled the integration of artificial intelligence (AI) into photovoltaic (PV) systems. this study proposes a novel PV paradigm utilizing fuzzy rule-based algorithms to automate monitoring and diagnosis of solar panel efficiency issues. By integrating AI, this approach offers improved accuracy and precision in evaluating PV system performance. the study investigates the impact of AI and fuzzy rule-based algorithms on PV system value by analyzing factors such as efficiency, light intensity, output electrical power, temperature, and humidity. this research compares the benefits and drawbacks of AI-based methods with conventional approaches, identifying potential applications and future directions. By exploring the integration of AI and fuzzy rule-based algorithms, this study contributes to the optimization and enhancement of photovoltaic systems. © 2024 IEEE.

关键词： Fuzzy Logic Maximum Power Point Tracking (MPPT) Neural networks (NNs) Photovoltaic (PV)

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Learning with Balanced Criss-Cross Attention for Cross-Modality Crowd Counting 23

Learning with Balanced Criss-Cross Attention for Cross-Modal...

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5th international conference on Information Technology and Computer Communications, ITCC 2023

作者： Zeng, Xin Zhang, Wanjun Wang, Huake Bian, Xiaoli Zhengzhou Vocational College of Finance and Taxation Zhengzhou China School of Computer and Information Engineering Henan University Kaifeng China School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798400700583

Cross-modality crowd counting is one of the most essential tasks in multimedia and image processing, which usually uses multi-sensor information as input in neural networks. Various approaches have been proposed to extract the alignment and relationships between the different modalities in the task of crowd counting. In this work, we explore how to further remedy the cross-modal discrepancies and learn latent relevance across different modalities. We present a novel RGBT crowd counting framework, namely Balanced Criss-Cross Attention Network (BCANet), to overcome the above limitations. To bridge the two modalities, we introduce a Balanced Criss-Cross Attention (BCA) module to encode complementary information across modalities. Lastly, we evaluate our BCANet via extensive experiments and demonstrate that it consistently achieves state-of-the-art results on RGBT-CC and DroneRGBT datasets. © 2023 ACM.

关键词： Image processing

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