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Real Time Spatial Sound Scene Analysis-AlertNet 3

Real Time Spatial Sound Scene Analysis-AlertNet

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international conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Nagaraj, S. Malathy, R. Sheeba, Merlin G. Jerusalem Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

This project introduces a groundbreaking approach to enhance the accuracy of acoustic scene classification and ensures user safety within music player applications. It provides a combined model of deep learning by merging Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM).The model uses Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) features as feature extractors, and processes these features to predict ***, this paper integrates the advanced scene classification model into music player applications. Users can enjoy their favourite music while the system continuously analyses the audio environment. When a potentially dangerous situation is detected, such as sirens, alarms, or other alerting sounds, the music playback is momentarily attenuated or paused, and users are promptly alerted to pay attention to their surroundings. Performance evaluation demonstrates 97% accuracy in a noisy environment and 98 % accuracy in clean *** CNN-Bi-LSTM hybrid model, named AlertNet, excels in capturing both fine-grained and contextual audio information. Its seamless integration into music players enhances safety and user experience. Finally, it exemplifies the fusion of cutting-edge deep learning techniques with everyday technology to ensure safety and situational awareness for users, especially in scenarios where distractions are common.

关键词： Audio feature extraction MFCC,GFCC acoustic signal processing CNN Bi-LSTM Alerting system

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Reconstruction of One-Dimensional signals and Two-Dimensional images Through the Use of Sobolev-Type Orthogonal Moments 4th

Reconstruction of One-Dimensional Signals and Two-Dimensiona...

引用

4th international conference on Digital Technologies and applications (ICDTA)

作者： El-khanchouli, Karim Bencherqui, Ahmed Joudar, Nour-Eddine Hafid, Abdelatif Sayyouri, Mhamed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Sidi Mohamed Ben Abdellah Univ Dept Math Modelling & Math Struct Lab FST Fes Morocco Higher Sch Engn Appl Sci Analyt Lab Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

We propose an innovative approach for the reconstruction of 1D signals and 2D images utilizing Sobolev-type orthogonal moments, specifically Krawtchouk-Sobolev moments KSOMs. This method capitalizes on unique discrete measures, leveraging the connection relations and difference equations that characterize these polynomial families. The weighted polynomials derived from Krawtchouk polynomials KOPs are applied in a sophisticated reconstruction algorithm. This approach significantly enhances accuracy and efficiency compared to traditional techniques, emphasizing the structural relationships and recurrent properties of the polynomials. The efficacy and performance of this technique are demonstrated through rigorous testing, marking a significant advancement in the field of signal processing and digital imaging.

关键词： Orthogonal polynomials discrete Sobolev polynomials difference equation 1D signal reconstruction 2D image reconstruction

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Analysing the Security and Privacy of Social Internet of Things and its applications 3

Analysing the Security and Privacy of Social Internet of Thi...

引用

3rd ieee international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Ranganthan, Chitra Sabapathy Kittappa, Thiagarajan Priya, A. Swetha Shankar, Bhukya Sheela, T. Sasikala, R. Ramachandran, G. Mphasis Corporation ChandlerAZ United States Rajalakshmi Institute of Technology Department of Mathematics Poonamalle Chennai India Amrita School of Engineering Department of Ece Karnataka India Cvr College of Engineering Department of Electronics and Communication Engineering Ibrahimpatnam Hyderabad India [Vinayaka Mission's Research Foundation Salem India Department of Mba Chennai600077 India

ISBN: (纸本)9798350361186

The globally dispersed, online-connected objects that are physically connected. Given that most smart devices are carried by individuals, we may use these individuals' social networks to protect data from being intercepted by unauthorized users, which is in line with the Social Internet of Things (SIoT) theory. To facilitate connection between devices and people as well as information sharing, SIoT blends social networks and IoT. New ecologies have emerged as a result of this convergence, which has improved both viewpoints. In fact, the lack of specially defined IoT protocols, along with the inherent flaws of SIoT devices, few resources, heterogeneous technology, and other factors, create an ideal environment for the proliferation of specialist cyber attacks. Since linked devices can result in significant amounts of personal data being captured and stored by businesses, the security of social IoT is a major concern. It is crucial for businesses to use security measures like encryption, authentication, authorization, and access control to protect data and guarantee privacy. In order to ensure that data is handled safely, firms should implement security guidelines and standards. This can involve installing firewalls and secure networks in addition to ordinary software upgrades. This paper gives a general overview of the SIoT's existing privacy challenges and requirements. © 2024 ieee.

关键词： Internet of things

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Application of image Weight Models to Increase Canny Contour Detector Resilience to Interference

Application of Image Weight Models to Increase Canny Contour...

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2023 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2023

作者： Lyasheva, Stella Safina, Rimma Shleymovich, Mikhail University Named after A.N. Tupolev-KAI Department for Automated Systems for Information Processing and Control Kazan National Research Technical Kazan Russia

ISBN: (纸本)9781665475952

Article discusses the issues of creating models and methods for detecting contours in images in control systems and automation of technological processes and objects based on computer vision technologies. An approach based on the construction and analysis of image weight models is proposed. This approach involves performing a discrete wavelet transform of the image and calculating weight values based on the detailing coefficients to assess the significance of changes in the pixels of copies of various levels of decomposition. The set of weight values is a weight model. Since significant changes are characteristic of contour points, the analysis of weight models makes it possible to detect them in images. It is also shown that the application of the Canny detector to the image weight model reduces the sensitivity to noise and the complexity of selecting the lower and upper thresholds. © 2023 ieee.

关键词： Computer vision

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image Spam detection in E-mails using Grasshoppers optimization technique

Image Spam detection in E-mails using Grasshoppers optimizat...

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2023 international conference on Computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Mallampati, Deepika Hegde, Nagaratna P Osmania University Dept. of CSE Telangana Hyderabad India Vasavi College of Engineering Dept of Cse Telangana Hyderabad India

ISBN: (纸本)9798350338003

one of the most frequent types of digital communication is email. Spam is unsolicited mass mail, whereas image spam includes spam text incorporated in images. This type of spam threatens email communications because spammers use it to avoid text spam filters. This paper provides a support vector machine (SVM) based classification algorithm and a biology optimization strategy to categorize emails as spam or ham. In this study, we examine image spam detection strategies using different combinations of image processing and machine learning algorithms. The grasshopper optimization algorithm (GOA) is an approach that statistically models and simulates the behavior of natural locust swarms. We investigate and analyze the performance of Grasshopper optimizations using several evaluation measures, such as accuracy, precision, recall, f1-score, and convergence rate to the global optimum solution. © 2023 ieee.

关键词： Support vector machines

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image Quality Assessment of Large Tissue Samples Stained using a Customized Automated Slide Stainer

Image Quality Assessment of Large Tissue Samples Stained usi...

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2023 ieee international Symposium on Medical Measurements and applications, MeMeA 2023

作者： Sithambaram, Prabhakar Kumarasami, Ramdayalan Sivaprakasam, Mohanasankar Joseph, Jayaraj Indian Institute of Technology Madras Dept. of Electrical Engineering Chennai India

ISBN: (纸本)9781665493840

Whole slide imaging (WSI) creates high-resolution digital images of entire tissue samples, enabling accurate and reliable analysis. Consistency in staining is essential for producing high-quality WSI slides. An automated slide stainer is critical in ensuring precise and uniform staining, especially for larger tissue sections where manual staining can be challenging. However, no large format stainers are currently available on the market. A custom-designed automated slide stainer for large tissue sections was developed to address these issues, having the capability to handle various glass slides from the standard 1' × 3' glass slides to the customized 2' × 3', 5' × 7' and 6' × 8' glass slides. The performance of the custom-designed large format automated slide stainer was compared to the Sakura Tissue-Tek Prisma machine and the manual staining method. The image quality of the whole slide images was evaluated using parameters like the signal-to-noise ratio and the consistency of the image histograms. The automated stainer showed a low intra-slide variation of stain intensity, with a lower standard deviation (SD) of 13.005 ± 1.515 compared to the manual method's SD of 17.315 ± 3.31. In addition, the automated slide staining showed a lower SD (Nissl = 1.795, Hematoxylin and Eosin (H&E) = 2.56) in signal-to-Noise Ratio (SNR) values compared to manual staining (Nissl = 6.282, H&E = 5.31), indicating excellent staining uniformity. Overall, the automated slide stainer had consistent staining across slides, with a low pairwise distance of 0.0070 ± 0.0017 (Nissl) and 0.0060 ± 0.0003 (H&E). Based on the image evaluation, the custom-designed large-format automated slide stainer was shown to be a reliable, repeatable, and consistent method for staining large tissue samples of sizes up to 150 × 200 mm. © 2023 ieee.

关键词： signal to noise ratio

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Enhancing Lifetime in Wireless Sensor Networks through image processing-Based Clustering with Genetic Algorithm Routing 4

Enhancing Lifetime in Wireless Sensor Networks through Image...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Ilampari, M. Amina Begum, M. Mahesh Babu, N. Gopi, S. Mythreyee, M. Deivasigamani, Shanmugavel SIMATS Saveetha School of Law India Oxford College of Engineering Department of ECE Chennai India Anurag Engineering College Suryapet Department of Information Technology Telangana Kodad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Dr. MGR Educational and Research Institute Department of EEE Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs. © 2024 ieee.

关键词： Sensor nodes

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Revitalizing ancient murals in the Shekhawati region through image inpainting techniques 11

Revitalizing ancient murals in the Shekhawati region through...

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11th ieee international conference on signal processing and Integrated Networks, SPIN 2024

作者： Yadav, Anshul Kumar Sharma, Sudhanshu Kumar, Rajesh Dhiraj CSIR-Central Electronics Engineering Research Institute Rajasthan Pilani India Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350308433

India has an extensive range of ancient artistic expressions in the form of mural paintings. These murals incorporate architectural features representing the cultural heritage of ancient civilizations. These paintings have declined over time due to human negligence and have deteriorated. The traditional approaches to restore this significant artwork are less effective and have been declared impotent. Thus, a practical digital inpainting framework is suggested to breathe new life into degraded murals. The suggested framework employs a two-step approach to attain optimal results in reconstructing murals for restoration purposes. In the first step, a semantic model is utilized to generate damage, defining masks that serve as inpainting parameters in various instances. The second step evaluates the effectiveness of three distinct inpainting architectures: PCONV, Conditional Texture Structure Dual Generation, and ***, this study provides a qualitative and quantitative comparison, utilizing metrics such as style loss, perceptual loss, SSIM, PSNR, and the perceptual quality of completeness. The results of the comparative analysis reveal that the T-former model outperforms others significantly, attaining an average SSIM of 0.9947 and a PSNR score of 45.3815 on test images. Moreover, the experiment's reconstruction, achieved by constraining all patches, successfully passes the visual inspection test, faithfully replicating the original mural. © 2024 ieee.

关键词： Restoration

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Union Embedding and Backbone-Attention boost Zero-Shot Learning Model (UBZSL) 5

Union Embedding and Backbone-Attention boost Zero-Shot Learn...

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5th ieee international conference on image processing applications and Systems (IPAS)

作者： Li, Ziyu Univ RWTH Aachen Germany

ISBN: (纸本)9781665462198

Zero-Shot Learning (ZSL) aims to identify categories that are never seen during training. There are many ZSL methods available, and the number is steadily increasing. Even then, there are still some issues to be resolved, such as class embedding and image functions. Human-annotated attributes have been involved in recent work on class embedding. However, this type of attribute does not adequately represent the semantic and visual aspects of each class, and these annotating attributes are time-consuming. Furthermore, ZSL methods for extracting image features rely on the development of pre-trained image representations or fine-tuned models, focusing on learning appropriate functions between image representations and attributes. To reduce the dependency on manual annotation and improve the classification effectiveness, we believe that ZSL would benefit from using Contrastive Language-image Pre-Training (CLIP) or combined with manual annotation. For this purpose, we propose an improved ZSL model named UBZSL. It uses CLIP combined with manual annotation as a class embedding method and uses an attention map for feature extraction. Experiments show that the performance of our ZSL model on the CUB dataset is greatly improved compared to the current model.

关键词： Zero-shot Learning image Classification Backbone-Attention CLIP

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Key Area Acquisition Training for Practical image-based Plant Disease Diagnosis 18

Key Area Acquisition Training for Practical Image-based Plan...

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18th ieee international Colloquium on signal processing and applications (CSPA)

作者： Odagiri, Kaito Shibuya, Shogo Cap, Quan Huu Iyatomi, Hitoshi Hosei Univ Grad Sch Sci & Engn Appl Informat Tokyo Japan

ISBN: (纸本)9781665485296

Automatic diagnosis of plant diseases using images is a fine-grained task, and disease symptoms are often ambiguous and highly variable. Pre-extraction of the region of interest (ROI) exhibiting disease symptoms (such as one or more leaves) is known to have a certain effect on improving accuracy. However, the ROI extraction at runtime is time-consuming, resulting in issues of system usability. This paper proposes a new training method called key area acquisition training (KAAT). KAAT reduces the variation in prediction results between images before and after the extraction of the ROI. By directing the model's attention to the ROI through learning, KAAT contributes to improved diagnostic performance without sacrificing execution time during diagnosis. In the evaluation, we conducted nine class diagnosis task (eight diseases and healthy) using 77K and 9K images of cucumber leaves (collected from different fields) for training and testing, respectively. The proposed KAAT improved diagnostic accuracy by 3.8% in macro-F1 and 2.0% in micro accuracy without increasing execution time.

关键词： automated plant diagnosis stability training convolutional neural networks segmentation ROI

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