检索结果-内蒙古大学图书馆

Enhanced Sound Recognition and Classification Through Spectrogram Analysis, MEMS Sensors, and PyTorch: A Comprehensive Approach 19th

Enhanced Sound Recognition and Classification Through Spectr...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Spournias, Alexandros Nanos, Nikolaos Faliagka, Evanthia Antonopoulos, Christos Voros, Nikolaos Keramidas, Giorgos Electrical and Computer Engineering Department University of the Peloponnese Patra Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031545207

The importance of sound recognition and classification systems in various fields has led researchers to seek innovative methods to address these challenges. In this paper, the authors propose a concise yet effective approach for sound recognition and classification by combining spectrogram analysis, Micro-Electro-Mechanical Systems (MEMS) sensors, and the Pytorch deep learning framework. This method utilizes the rich information in audio signals to develop a robust and accurate sound recognition and classification system. The authors outline a three-stage process: data acquisition, feature extraction, and classification. MEMS sensors are employed for data acquisition, offering advantages such as reduced noise, low power consumption, and enhanced sensitivity compared to traditional microphones. The acquired audio signals are then preprocessed and converted into spectrograms, visually representing the audio data’s frequency, amplitude, and temporal attributes. During feature extraction, the spectrograms are analyzed to extract significant features conducive to sound recognition and classification. The classification task is performed using a custom deep learning model in Pytorch, leveraging modern neural networks’ pattern recognition capabilities. The model is trained and validated on a diverse dataset of audio samples, ensuring its proficiency in recognizing and classifying various sound types. The experimental results demonstrate the effectiveness of the proposed method, surpassing existing techniques in sound recognition and classification performance. By integrating spectrogram analysis, MEMS sensors, and Pytorch, the authors present a compact yet powerful sound recognition system with potential applications in numerous domains, such as predictive maintenance, environmental monitoring, and personalized voice-controlled devices. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

引用

IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Multi-Robot Navigation Uses the Greedy Heuristic Algorithm Based on the ACO Algorithm

Multi-Robot Navigation Uses the Greedy Heuristic Algorithm B...

引用

2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Zaini, Ahmad Muhtadin Rioza, Hansen Ade Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350368802

Robot swarm navigation faces challenges in coordi-nation in dynamic environments with limited information, since each robot only has local access, making optimal paths difficult to achieve. Increasing the number of robots also increases the complexity of communication and decision-making, while energy efficiency remains a priority. The Ant Colony Optimization (ACO) algorithm mimics the behavior of ants through pheromone trails, allowing robots to leave virtual trails that guide other robots in forming optimal paths, allowing them to adapt to changing environments. In this paper, we propose an optimal multi-robot navigation system with ACO based on Greedy Heuristics to improve the efficiency of robots in dealing with static and dynamic obstacles, avoiding collisions, and preventing deadlocks. The test results obtained the shortest distance of 31.2132034 meters with alpha parameters of 3 points and beta of 4 points as the optimum parameter configuration according to the method used. © 2024 IEEE.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

ENERGY AND COST BENEFITS OF ADVANCED LIGHTING TECHNOLOGY APPLICATIONS IN TRAFFIC LIGHT SYSTEMS 14

ENERGY AND COST BENEFITS OF ADVANCED LIGHTING TECHNOLOGY APP...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Polycarpou, Alexis Georgiou, Marios S. Department of Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus Electromechanical Section Nicosia Municipality Nicosia Cyprus

ISBN: (纸本)9781837242689

Following a pilot project, which concluded in 2016, the replacement of old energy consuming Traffic Light Systems (TLS) of Nicosia Municipality were the subject of an investigation with regards to the energy saving potential and most importantly the payback period of the investment, through presumptions. This paper presents the results obtained of the new efficient technology implementation, with regards to the energy saving that materialised in the Nicosia Municipality. Investigation is presented in an attempt to determine whether the proposed solution and calculated capital employed had the expected energy usage reduction, return on investment, and payback period. The estimated cost benefit of the installed systems compared to the actual benefits varies significantly, after factors such as maintenance and electricity costs are considered. © The Institution of engineering & Technology 2024.

关键词： Street lighting

来源：评论

学校读者我要写书评

暂无评论

Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

引用

Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation the Prayer Reminder Application using KISS Principle based on User Centered Design 7

Design and Implementation the Prayer Reminder Application us...

引用

7th Information Systems International Conference, ISICO 2023

作者： Yunanto, Andhik Ampuh Putri, Febi Fidhiyanti Permatasari, Desy Intan Nailussa'ada Hardiansyah, Fadilah Fahrul Sa'adah, Umi Aziz, Adam Shidqul Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Keputih Sukolilo Surabaya East Java60111 Indonesia

The five daily prayers are an obligation of worship for Muslims in the world. Prayers have a predetermined time schedule according to the teachings of the Prophet Muhammad SAW and have different schedules according to each region. As Muslims, it is obligatory to pray five times a day. However, there are a lot of start-up company which forgot about the employee need such as praying. One of them is called byPulsa Start-up company which wants anticipates delays in praying. Because of this reason, the Ceo at byPulsa need an application to remind the employee about praying. So, this company wants to improve the additional feature into its application and develop its application more less interaction and efficient. So, this research aims to develop the previous application into an application that can remind employees to pray. In this research, Kiss Principle Method and User Centered Design were used to this application to make it more efficient and usable for user. The results show that the user feels more satisfied with the application because it has prayer schedule feature based on user's position. The CSAT score from evaluation has score 80% which higher than previous application. This application can help and guide users in exploring their religion by following online studies and using features such as prayer beads, Islamic book recommendations, even Islamic podcasts. As well as the Kiss principle method used here, it functions to make it easier for users to interact with the application. © 2023 The Authors. Published by Elsevier B.V.

关键词： User centered design

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Memory-Based Collaborative Filtering and Deep Learning Models for Resolving Cold Start and Data Sparsity Issues in E-commerce Recommender Systems 3

Comparative Analysis of Memory-Based Collaborative Filtering...

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Shetty, Amrithkala M Manjaiah, D.H. Aljunid, Mohammed Fadhel Yogesh, K.M. Mangalore University Department of Computer Science Karnataka India Thamar University Computer and Informatics Center Thamar Yemen M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350384598

In today's digital landscape, where online activities and e-commerce play pivotal roles, Recommender Systems (RS) have become indispensable tools for enhancing user experiences by providing personalized recommendations. However, collaborative filtering (CF) algorithms often face challenges such as the cold start problem and data sparsity. This work addresses these challenges using memory-based CF approaches such as Singular Value Decomposition (SVD), SVD++, K-Nearest Neighbors (KNN) Baseline, and K-Nearest Neighbors (KNN) Basic. Hyperparameters for these algorithms are optimized using GridSearchCV with cross-validation values set to 5, 10, and 15. Additionally, Deep Learning (DL) models including LSTM, BiLSTM, Att+BiLSTM, User-Product Fusion (UPF), and Deep Collaborative Filtering (DCF) are explored. The BiLSTM model achieves a lower Root Mean Squared Error (RMSE) of 1.1224, while the DCF model achieves a Mean Absolute Error (MAE) of 0.6897. Among the evaluated models, SVD++ achieves an RMSE of 0.9881 and an MAE of 0.7003 with cross-validation set to 15, indicating superior predictive accuracy. The best performance is observed with cross-validation set to 15. These results highlight the effectiveness of optimized memory-based CF algorithms in overcoming the cold start and data sparsity issues, leading to more accurate and reliable recommendations. The findings suggest that memory-based CF algorithms, specifically SVD++, can significantly enhance the development of robust recommender systems, ultimately improving user experience in e-commerce and other online platforms. © 2024 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Cerebral cortex contributions to Parkinson's disease tremor:what exactly generates the tremor?

引用

Neural Regeneration Research 2025年第12期20卷 3535-3536页

作者： Manuel Bange Hao Ding Muthuraman Muthuraman Informatics for Medical Technology Institute of Computer ScienceUniversity AugsburgAugsburgGermany Department of Neurology Neural Engineering with Signal Analytics and Artificial Intelligence(NESA-AI)University Clinic WürzburgWürzburgGermany

Pathological tremor is one of the cardinal symptoms in Parkinson's disease (PD).Tremor is comprised of involuntary,rhythmic,a nd oscillating movements that can vary according to the circumstances under which they occur,the body parts that are involved,and the frequency at which they *** example,tremors can be mild to severe,are stress sensitive,and can affect arms,legs,or the head (Dirkx and Bologna,2022).

关键词： tremor cortex exactly

来源：评论

学校读者我要写书评

暂无评论

Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Allocation in Fog Computing for Healthcare Applications

引用

computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：