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Indoor Sound Classification Using Lightweight CNN with real-time IoT Integration

Indoor Sound Classification Using Lightweight CNN with Real-...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Siddharth Amarnath Bethi Pardhasaradhi Venkata Siva Sai Anil Kumar Sagar Koorapati Linga Reddy Cenkeramaddi ICT Dept University of Agder Grimstad Norway ICT Dept Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education India CPU Design Group Qualcomm Technologies Austin TX

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This paper presents a lightweight Convolutional Neural Network (CNN) for indoor sound classification, specifically designed to identify ten distinct classes: knock on the door, mouse click, keyboard typing, wooden door squeaks, opening the can, washing machine, vacuum cleaner, alarm clock, clock ticking, and broken glass from [1]. The sound signals were captured using a microphone and transformed from the time domain to time-frequency images through Continuous Wavelet Transform (CWT). The proposed CNN, with a compact model size of 2.1 MB, was benchmarked against several well-known pre-trained models, including DenseNet, EfficientNet, InceptionNet, MobileNet, NASNet, ResNet, VGGNet, and Xception. Experimental results demonstrate that our lightweight CNN significantly outperformed these models, achieving a validation accuracy of 80% in a ten-fold cross-validation framework. The best performance among the pre-trained models was observed with ResNet101V2, which achieved only 62% validation accuracy in a ten-fold cross-validation framework with a considerably larger model size of 163 MB. Furthermore, the proposed model was successfully deployed in real-time on various IoT and processor modules, including the Raspberry Pi, NVIDIA A100, Tesla V100, and Intel Xeon Platinum, where it demonstrated superior efficiency, running at just 35 milliseconds per inference on the Raspberry Pi edge device. The lightweight CNN’s high accuracy and low latency make it an ideal candidate for applications in smart home systems, security monitoring, and real-time audio event detection on resource-constrained devices.

关键词： time-frequency analysis Accuracy Continuous wavelet transforms Platinum Keyboards Smart homes real-time systems Convolutional neural networks Security Monitoring

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A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone

A Computer Vision Approach for Autonomous Cars to Drive Safe...

引用

international Image Processing, applications and systems conference (IPAS)

作者： Abu Shad Ahammed Md Shahi Amran Hossain Roman Obermaisser Chair of Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.

关键词： YOLO Computer vision Computational modeling Roads Urban areas Robustness Smart transportation Automobiles Autonomous vehicles Automotive engineering

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Application of Multilayer Cascade Fusion Enhanced Network Model (MLCF-Net) to Underwater Image Processing

Application of Multilayer Cascade Fusion Enhanced Network Mo...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Kexuan Lin Xiran Sun Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In this paper, a multilayer cascade fusion enhancement network model (MLCF-Net) is proposed with the aim of improving the quality of underwater images. Firstly, by classifying the degradation phenomena of underwater images, the study analyzes the causes of bias, blur and low light, and quantifies the characteristics of different degraded images using texture-aware joint color histogram analysis. Based on this, for each degradation case, this paper designs corresponding enhancement strategies: using white balance correction technique to resolve the off-color, Laplace filtering with deep learning super-resolution model to recover blurring details, and using adaptive histogram equalization (CLAHE) to enhance low-light images. Finally, MLCF-Net effectively copes with the coexistence of multiple degradation phenomena in complex scenes through three modules-color correction, texture enhancement, and luminance compensation-combined with a cascade fusion strategy.

关键词： Degradation Histograms Adaptation models Systematics Image color analysis Computational modeling Superresolution Nonhomogeneous media real-time systems Power electronics

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Improvement and Performance of Data Transmission Algorithms in Computer Communications

Improvement and Performance of Data Transmission Algorithms ...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Yi Liu Hao Liu Yun Cheng Siqiang Cheng Information School Hunan University of Humanities Science and Technology Loudi China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In response to the current problems of low data transmission efficiency, high latency, and insufficient stability in computer communications, this paper studies a comprehensive optimized data transmission algorithm, which aims to improve network resource utilization efficiency and transmission performance and reduce the frequency of congestion by dynamically adjusting the transmission rate, adaptive congestion control and queue management optimization. Firstly, a dynamic rate adjustment mechanism based on real-time network status detection is designed to monitor the network bandwidth, latency, and packet loss rate in real time to dynamically adjust the data packet transmission rate. Secondly, an adaptive congestion control strategy is applied to reduce the impact of congestion on data transmission by monitoring the network congestion status in real time and adjusting it according to the changing trend. Finally, queue management and priority scheduling algorithms are used to reasonably arrange the transmission order of data packets, reduce queuing delays, and improve the transmission efficiency of high-priority data packets. In terms of latency and throughput, as latency increases, throughput gradually decreases. Taking the bandwidth of 100Mbps as an example, when the network latency is 20ms, the throughput is 92.15Mbps, and when the latency is 100ms, the throughput drops to 74.11Mbps. The optimized data transmission algorithm proposed in this paper significantly improves the efficiency and stability of network transmission by dynamically adapting to network conditions. In future research, the application of this algorithm can be further expanded, and its adaptability and robustness in more complex network environments and high-load applications can be enhanced to improve the reliability of data transmission.

关键词： Adaptive systems Heuristic algorithms Packet loss Complex networks Bandwidth Throughput real-time systems Stability analysis Data communication Thermal stability

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Conformance-Driven Anomaly Detection for Cyber-Physical systems 25

Conformance-Driven Anomaly Detection for Cyber-Physical Syst...

引用

Proceedings of the ACM/ieee 16th international conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： Xin Qin California State University Long Beach Long Beach California USA

ISBN: (纸本)9798400714986

The rapid integration of learning-enabled components into safety-critical systems has created a rising demand for robust frameworks to ensure operational reliability and safety. Current approaches, which focus on enhancing robustness during training or implementing self-monitoring mechanisms, remain vulnerable to adversarial manipulation of system inputs or internal data flows, exposing risks of malicious exploitation. To address this gap, we propose a novel third-party monitoring framework that independently evaluates cyber-physical systems safety by statistically analyzing real-time behavior against training behaviors recorded in historically safe operational contexts. Our method employs stochastic conformance testing to account for environment and system stochasticity, enabling comparisons between observed behaviors and validated behaviors. We demonstrate our proposed pipeline using a cartpole example in which reinforcement learning serves as the system controller.

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AI-Enhanced Fluid Dynamics for Smart Industrial systems: real-time Pressure and Velocity Estimation Using Fusion Networks

AI-Enhanced Fluid Dynamics for Smart Industrial Systems: Rea...

引用

ieee international conference on Consumer Electronics (ICCE)

作者： Vishnu Mohan M. S. Vivek Menon Hariprasad M. P. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Advancements in AI-driven fluid dynamics are opening new frontiers for estimating fluid properties in smart industrial systems, with promising applications in consumer electronic systems ranging from wearable health monitors to smart home appliances. Such methods, initially centered around machine learning (ML) and subsequently deep learning (DL), offer significantly faster and more accurate estimates compared to traditional approximation-based methods. This study presents two novel DL models that predict fluid flow parameters, specifically pressure, and velocity, within flow fields with different geometric profiles. Our first model learns specific fluid behaviors based on distinct geometries, while our second model employs a fusion architecture to generalize across multiple geometries. Based on our extensive evaluation, we observe that individual models estimate pressure and velocity with high accuracy for distinct geometries, while the fusion model generalizes well across multiple geometric profiles, demonstrating greater practical applicability. This research underscores the potential of DL models to improve fluid dynamics prediction, providing a promising alternative that bypasses the need for physics-guided systems. Our approach bridges industrial-level accuracy with consumer needs, enabling more efficient, adaptive, and intelligent products for everyday use.

关键词： Geometry Deep learning Adaptation models Accuracy Fluid dynamics Smart homes Predictive models real-time systems Consumer electronics Monitoring

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Classification and Detection of Power Quality Disturbances using Support Vector Machine

Classification and Detection of Power Quality Disturbances u...

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Spiel Sahoo Adrija Dutta Sanhita Mishra Rudra Narayan Dash Tapaswini Biswal Sarat Chandra Swain SoEE KIIT DU Bhubaneswar

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This paper explores the classification of power quality disturbances using machine learning models. It is started by generating signals that represent different types of disturbances and then processing them using Discrete Wavelet Transform with Daubechies wavelets to extract important features. A dataset has been created from these processed signals and used for training and testing with machine learning models. Among various techniques, Support Vector Machines (SVM) is opted for classification due to its accuracy and efficiency. Since power quality disturbances may lead to serious issues like system instability and equipment failure, having a reliable classification system is crucial. Here, SVM Kernel performs well while categorizing power quality disturbances.

关键词： Support vector machines Training Accuracy Power quality Feature extraction real-time systems Discrete wavelet transforms Reliability Kernel Testing

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Spatio-Temporal Mixed Graph Neural Controlled Differential Equations with Adaptive Connection Sampling for Irregular Multivariate time Series Anomaly Detection

Spatio-Temporal Mixed Graph Neural Controlled Differential E...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xudong Jia Wei Peng Chiran Shen Baokang Zhao Peng Xun College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series data often demonstrate sparse and irregular characteristics in real-world signal processing applications, making anomaly detection challenging. This paper introduces STMG-AD, a spatio-temporal mixed graph neural controlled differential equation method with adaptive connection sampling, designed specifically for anomaly detection in irregular multivariate time series. By integrating causal graphs with graph attention networks and employing adaptive connection sampling coupled with Monte Carlo dropout, STMG-AD can enhance the robustness and accuracy of anomaly detection. Experiments on various real-world datasets demonstrate its superiority over existing methods in detecting anomalies in irregular multivariate time series.

关键词： Uncertainty Monte Carlo methods Adaptive systems Accuracy time series analysis Differential equations Signal processing Robustness Speech processing Anomaly detection

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Research on the Application of Chinese Language Database in Intelligent Communication System

Research on the Application of Chinese Language Database in ...

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Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Sun Luyao Liu Qian Niu Jin Shandong Vocational and Technical University of Engineering Jinan China Biobase Jinan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This study proposes an innovative Chinese language information extraction algorithm, which combines Bi-LSTM and CRF, aiming to improve the processing accuracy and efficiency of Chinese language in intelligent communication system. Bi-LSTM captures contextual information in sentences through two LSTM networks, forward and reverse, to solve the shortcomings of traditional LSTM in sequence modeling; The CRF makes the optimization of the label sequence output by Bi-LSTM, and the precision of named entity recognition, word segmentation and syntax analysis can be improved greatly. Experimental results show that the accuracy of the system based on this algorithm in the named entity recognition task is 92.4%, which is more than 6% higher than the traditional HMM model. At the same time, the model shows higher robustness and lower error rate in the processing of long texts and complex semantic relationships. On multiple test data sets, the F1 score and recall rate of the algorithm are also greatly improved, and the inference time is kept within the range required for real-time communication.

关键词： Training Analytical models Accuracy Communication systems Databases Semantics Hidden Markov models Bidirectional long short term memory Named entity recognition Conditional random fields

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A Deep Learning and Transfer Learning-Based Application for Skin Cancer Classification

A Deep Learning and Transfer Learning-Based Application for ...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Sabrina Karboua Fouzi Harrag Sarrah Karboua Amal Booutadjine Mohamed Deriche Department of Computer Science Setif 1 University-Ferhat ABBAS Abdelhamid MEHRI - Constantine 2 University Algeria College of Engineering and Information Technology AIRC Ajman University UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Skin cancer is the most prevalent type of cancer worldwide, accounting for approximately one-third of all cancer cases, with its incidence steadily rising in recent years. This is why early and precise identification of skin cancer is crucial for effective treatment and enhancing survival rates. Leveraging advancements in deep learning and transfer learning, we developed a comprehensive system to address this challenge. In this study, we employed the HAM10000 dataset, a benchmark resource for skin cancer research, to train and evaluate deep learning models. Two architectures, DenseNet201 and ResNet50, were fine- tuned and modified to enhance their accuracy in classifying skin lesions as benign or malignant. Rigorous preprocessing, data augmentation, and model fine-tuning were utilized to address challenges such as dataset imbalance and improve classification accuracy. Our experiments revealed that the modified fine-tuned DenseNet201 model achieved superior performance compared to fined-tuned ResNet50 and other leading models reviewed in the literature with an accuracy of 95.04%. This model was then integrated into a mobile application, “SkinSafe,” to provide an accessible and user-friendly tool for real-time skin cancer classification.

关键词： Deep learning Accuracy Transfer learning Skin real-time systems Data models Mobile applications Lesions Skin cancer Residual neural networks

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