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A Scalable Framework for IoT applications in Cloud Environments 10

A Scalable Framework for IoT Applications in Cloud Environme...

引用

10th international conference on signal processing and Communication, ICSC 2025

作者： Gupta, Tushar Bhambhani, Shirin

ISBN: (纸本)9798331511890

The exponential growth of the Internet of Things (IoT) has introduced significant challenges in managing scalability, availability, and efficiency. With billions of interconnected devices generating vast amounts of data, traditional frameworks struggle to handle the complex requirements of modern IoT applications. Addressing these challenges is crucial to fully leverage the potential of IoT in various domains, including smart homes, healthcare, and industrial automation. This paper proposes a novel framework for integrating IoT applications with cloud environments to achieve scalability and high availability. The framework is structured into four layers: the device layer leverages Message Queuing Telemetry Transport (MQTT) as a low overhead communication protocol;the edge gateway layer aggregates data using lightweight Kubernetes;the ingestion and cloud infrastructure layer employs Kafka and Apache Spark for ingesting data and data transformation;and the data processing and analytics layer utilizes the cloud infrastructure layer to send the data to databases for data visualization. By leveraging cloud environments, this solution enhances scalability, availability, and overall system robustness. This paper also explores the challenges involved in implementing such an architecture, and provides insights into future advancements in IoT cloud integration. © 2025 ieee.

关键词： Cloud Computing Internet of Things (IoT) IoT architecture

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Analysis and Optimization of Care-purpose Drone For Medical-Based applications 20

Analysis and Optimization of Care-purpose Drone For Medical-...

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20th ieee international Colloquium on signal processing and Its applications, CSPA 2024

作者： Hanafiah, Muhammad Hariz Mohd Al-Humairi, Safaa Najah Saud Taib, Mohamad Amirul Aznil Mohd Jaharadak, Adam Amril Management And Science University Faculty Of Information Sciences And Engineering Selangor Shah Alam40100 Malaysia

ISBN: (纸本)9798350382310

This paper explores the analysis and optimization of efficient drones with the primary aim of timely and precise item delivery. Unmanned aerial vehicles (UAVs), commonly known as drones, operate autonomously through remote control or software-managed flight plans, incorporating onboard sensors and a global positioning system (GPS). Originally developed for military purposes, drones have diversified into civilian applications, particularly within the medical and care-related domains, which are the focus of this research. The medical field faces challenges such as limited road access in urban areas, restricted entry to contagious disease zones, challenging terrain for human access, deployment in war zones, extended transportation times, and constrained operating hours. The utilization of drones addresses these challenges by facilitating real-time data collection, cost-effective payload delivery, and rapid advancements across commercial, industrial, and recreational sectors. Despite their potential, the integration of drone technology into the medical domain has been slow. Drones play a significant role in epidemiological tasks, including disease surveillance, disaster monitoring, and biological hazard surveillance. Telemonitoring, perioperative assessments, remote diagnostics, and treatment are enhanced through telecommunication drones. Moreover, drones exhibit immense potential as reliable platforms for transporting medications, vaccinations, emergency medical equipment, laboratory samples, pharmaceuticals, and even patients. Recognizing the national priority of drone utilization, ongoing efforts involve robust research initiatives in safety, business expansion, heightened public awareness, and community involvement. Deploying delivery drones in areas inaccessible to humans, such as those with highly contagious diseases or war zones, streamlines operations and mitigates potential risks associated with human involvement. In conclusion, the application of delivery dron

关键词： Drones

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Discrimination of Emotional Valence Based on Physiological signals in the Hand and Machine Learning Algorithm 5

Discrimination of Emotional Valence Based on Physiological S...

引用

5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Zhang, Qiuhao Xu, Jing Jin, Huibin Dai, Shiying Liu, Yihan Shen, Yixuan Li, Yang School of Artificial Intelligence Ningbo Polytechnic Ningbo Maker Technology Co. Ltd Ningbo China Mechanical & Electrical Engineering Institute Ningbo Polytechnic Ningbo China International Business Travel Faculty Ningbo Polytechnic Ningbo China

ISBN: (纸本)9798350374407

As a basic function of the human brain, emotions are an important factor influencing people's behavior and decision-making. Physiological signals come from the autonomic nervous system activity in the human body, and its change law is closely related to the physiological and emotional activity of the human body, so it can be used as a basis for accurate judgment of emotional state. At present, the emotion analysis method based on machine intelligence has become a research hotspot. In this paper, the skin electrical signals, pulse wave signals and blood oxygen saturation signals in the hand are analyzed, and a machine learning model is constructed to distinguish positive and negative emotions. 50 college students received emotional induction based on video clips of different emotional colors, and truthfully reported their emotional status. The SC signal, pulse wave and blood oxygen saturation signal are collected synchronously through the finger sleeve. By extracting the time domain and frequency domain feasures of the SC signal and its difference, the time domain and morphological features of the pulse wave signal, the statistical values and change rate of the blood oxygen saturation signal, the support vector machine is selected as the classifier. Finally, the accuracy of distinguishing positive and negative is 80.5%.The F1 score is 0.784. The results show that the emotional valence discrimination model based on SC signals and pulse oxygen saturation signals can effectively identify emotions. © 2024 ieee.

关键词： Support vector machines

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Construction of image Recognition Model Based on Second-Order Local Feature Aggregation 3

Construction of Image Recognition Model Based on Second-Orde...

引用

3rd ieee international conference on Power, Electronics and Computer applications, ICPECA 2023

作者： Min, Gang Shu, Yajie Zhang, Qian Zhao, Huaju Shu, Zhong Jingmen Mobile Media Co. Ltd Jingmen Daily Hubei Jingmen China Jingchu University of Technology School of Electronic Information Engineering Hubei Jingmen China

ISBN: (纸本)9781665472784

This paper analyzes the mathematical model constructed by the Net VLAD method, and proposes a second-order function based on the Net VLAD method to solve the problem that the obtained image feature encoding is a first-order function of the local information of the image. image feature encoding learning dictionary. Use the Net VLAD classifier as the fully connected layer of the CNN model, design the reconstruction residual weight, and redesign the image feature extraction module for the control of the reconstruction residual value range;when the image feature encoding in the Net VLAD learning dictionary is described by a first-order vector, there is a problem that it is difficult to derive and calculate the minimum value of image features. In the image dimensionality reduction transformation kernel, the first-order linear function is promoted to a second-order nonlinear function, and the image dimensionality reduction transformation kernel is used as a breakthrough, and the second-order nonlinear transformation kernel is used. The activation function has the characteristic of being derivable, and the local features of the image samples are subjected to dimensionality reduction processing to achieve more accurate dimensionality reduction calculations for image features. In the experiment, the accuracy of image feature classification, the recognition rate of image samples, and the training and testing efficiency of image samples are used as verification data. By using the CNN deep convolutional neural network model, the Net VLAD learning dictionary and the improved learning dictionary proposed in this paper, the Multiple image sample sets are compared for training and testing, which verifies that the improved learning dictionary based on Net VLAD proposed in this paper has obvious advantages. © 2023 ieee.

关键词： image enhancement

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IPAS 2025 Abstract Page

IPAS 2025 Abstract Page

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international image processing, applications and Systems conference (IPAS)

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Lung Cancer Classification using ResNet50 2

Lung Cancer Classification using ResNet50

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Nasra, Para Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Lung cancer is still the most often occurring and lethal form of cancer globally, contributing significantly to the annual death from cancer. With the use of efficient and automated testing technologies, the project seeks to lower mortality rates and improve early detection accuracy. Improving survival rates requires early diagnosis, but because current diagnostic techniques such as imaging and biopsies rely so much on clinical presentation, they frequently lead to late diagnoses. One of the most efficient deep learning architectures for applications involving image recognition is Convolutional Neural Networks. A version of the ResNet (Residual Networks) architecture known as ResNet50 has become well-known for its capacity to lessen the vanishing gradient issue and make intense network training possible. ResNet50, which has been pre-trained on imageNet, is used in this study as a feature extractor to detect lung cancer. The outcomes of the experiments indicate that the ResNet50-based model is highly accurate in differentiating between malignant lung tissues and those that are not. In addition to Accuracy, the model's efficiency in managing and analyzing images offers significant advantages over traditional diagnostic methods. The application of ResNet50 to the diagnosis of lung cancer demonstrates the Deep learning transformative potential models in the field of cancer diagnostics. This study provides a reliable, accurate, and valuable diagnostic tool, which contributes to the ongoing efforts to improve the early identification and treatment outcomes for patients with lung cancer. Lung tumor identification and diagnosis have greatly benefited from the use of deep learning and convolutional neural networks. These technologies improve the precision and effectiveness of medical imaging analysis by utilizing advanced image processing and pattern recognition capabilities.

关键词： ResNet50 Convolutional Neural Network Transfer Learning Deep Learning Lung Cancer image Classification

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KD-Former: Transformer Knowledge Distillation for image Matting

KD-Former: Transformer Knowledge Distillation for Image Matt...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ziwen Li Bo Xu Cheng Lu OPPO Research Institute XPENG

Vision transformers (ViTs) have been outstanding in multiple dense prediction tasks, including image matting. However, the high computational and training costs of ViTs lead to a bottleneck for applications on low computing power devices. In this paper, we propose a novel transformer-specific knowledge distillation (KD-Former) framework for image matting that can effectively transfer core attribute information to improve the lightweight transformer model. To enhance the information transfer effectiveness in each stage of Vits, we rethink transformer knowledge distillation via dual attribute distillation modules - Token Embedding Alignment (TEA) and Cross-Level Feature Distillation (CLFD). Extensive experiments demonstrate the effectiveness of our KD-Former framework and each proposed key component. Our lightweight transformer-based model outperforms the state-of-the-art (SOTA) matting models on multiple datasets.

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Impact of image Pre-processing in image Quality and Segmentation for Banana Leaf Disease Prediction

Impact of Image Pre-Processing in Image Quality and Segmenta...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Abisha, A. Bharathi, N. Srm Institute of Science and Technology Department of Computer Science and Engineering vadapalani Campus No.1 Jawaharlal Nehru Rd TN Vadapalani India

ISBN: (纸本)9798350313987

Recognizing and obtaining appropriate treatments for pest and disease infections in agriculture hinges on the accurate detection of leaf diseases. The key to the detection lies in image preprocessing and segmentation, which play a pivotal role in isolating the disease from the surrounding leaf background. Consequently, this research study undertakes the task of image preprocessing and segmentation. To identify the most suitable segmentation method for leaf analysis, several approaches, including Thresholding, Morphological Transformation (MT), Contour detection, Canny edge detection, Sobel, and k-means, are thoroughly examined and compared in the study. The effectiveness of these preprocessing and segmentation techniques is assessed using quantitative metrics such as Root Mean Square Error (RMSE), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The results of the investigation reveal that the k-means approach outperforms all other methods. It achieves a significantly lower RMSE value of 0.0013, a higher PSNR value of 57.41, and an SSIM value of 0.9982. Furthermore, the study demonstrates that image preprocessing has no adverse effects on image quality, and the k-means method emerges as the optimal choice for disease segmentation in banana leaves. © 2023 ieee.

关键词： Banana Leaf image Plant disease Plant health Pre-processing Segmentation

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Fast Blue-Noise Generation via Unsupervised Learning

Fast Blue-Noise Generation via Unsupervised Learning

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ieee international conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Giunchi, Daniele Sztrajman, Alejandro Steed, Anthony UCL Comp Sci Dept London England

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Blue noise is known for its uniformity in the spatial domain, avoiding the appearance of structures such as voids and clusters. Because of this characteristic, it has been adopted in a wide range of visual computing applications, such as image dithering, rendering and visualisation. This has motivated the development of a variety of generative methods for blue noise, with different trade-offs in terms of accuracy and computational performance. We propose a novel unsupervised learning approach that leverages a neural network architecture to generate blue noise masks with high accuracy and real-time performance, starting from a white noise input. We train our model by combining three unsupervised losses that work by conditioning the Fourier spectrum and intensity histogram of noise masks predicted by the network. We evaluate our method by leveraging the generated noise for two applications: grayscale blue noise masks for image dithering, and blue noise samples for Monte Carlo integration.

关键词： Neural networks image processing Noise generators Monte Carlo methods

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Dual-Port CMOS image Sensor with Regression-Based HDR Flux-to-Digital Conversion and 80ns Rapid-Update Pixel-Wise Exposure Coding

Dual-Port CMOS Image Sensor with Regression-Based HDR Flux-t...

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2023 ieee international Solid-State Circuits conference, ISSCC 2023

作者： Gulve, Rahul Rangel, Roberto Barman, Ayandev Nguyen, Don Wei, Mian Sakr, Motasem A. Sun, Xiaonong Lindell, David B. Kutulakos, Kiriakos N. Genov, Roman University of Toronto Toronto Canada

ISBN: (纸本)9781665428002

Today's best consumer cameras typically use computational imaging techniques to digitally enhance images by means of software post-processing to yield both high fidelity and low cost. A common technique, for example, is to combine multiple shots with different camera settings into one enhanced image that has a high dynamic range (HDR). This post-processing-based approach fails when fast motion and/or rapidly changing illumination are present, as often happens in autonomous driving, drone imaging, and action-camera applications, or when the illumination itself is actively controlled (e.g., for depth sensing). These applications require a much tighter temporal integration of: (1) in-pixel processing, (2) pixel readout, and (3) post-capture enhancement. Aiming to address these needs, a new class of 'coded' computational image sensors has emerged, with both fine (i.e., per-pixel) [1-3] and coarse (i.e., per-pixel-cluster) [4] programmable exposure control. Some of these sensors offer spatial exposure control for single-shot HDR imaging [1], [2], but require multiple ADC types and/or a number of pre- and postprocessing steps (e.g., adaptive pixel-wise exposure control, HDR reconstruction, etc.). Other coded sensors support a variety of computational imaging techniques (e.g., robust depth imaging, compressed sensing [3]), but their conventional ADCs do not offer HDR readout. HDR sensors exist that digitize the pixel output during exposure, before it saturates, but offer no coding [5], [6]. © 2023 ieee.

关键词： Pixels

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