IEEE Open Journal of the Computer Society最新文献_第8页

Musical Genre Classification Using Advanced Audio Analysis and Deep Learning Techniques 利用高级音频分析和深度学习技术进行音乐流派分类

IEEE Open Journal of the Computer Society Pub Date : 2024-07-19 DOI: 10.1109/OJCS.2024.3431229

Mumtahina Ahmed;Uland Rozario;Md Mohshin Kabir;Zeyar Aung;Jungpil Shin;M. F. Mridha

{"title":"Musical Genre Classification Using Advanced Audio Analysis and Deep Learning Techniques","authors":"Mumtahina Ahmed;Uland Rozario;Md Mohshin Kabir;Zeyar Aung;Jungpil Shin;M. F. Mridha","doi":"10.1109/OJCS.2024.3431229","DOIUrl":"10.1109/OJCS.2024.3431229","url":null,"abstract":"Classifying music genres has been a significant problem in the decade of seamless music streaming platforms and countless content creations. An accurate music genre classification is a fundamental task with applications in music recommendation, content organization, and understanding musical trends. This study presents a comprehensive approach to music genre classification using deep learning and advanced audio analysis techniques. In this study, a deep learning method was used to tackle the task of music genre classification. For this study, the GTZAN dataset was chosen for music genre classification. This study examines the challenge of music genre categorization using Convolutional Neural Networks (CNN), Feedforward Neural Networks (FNN), Support Vector Machine (SVM), k-nearest Neighbors (kNN), and Long Short-term Memory (LSTM) models on the dataset. This study precisely cross-validates the model's output following feature extraction from pre-processed audio data and then evaluates its performance. The modified CNN model performs better than conventional NN models by using its capacity to capture complex spectrogram patterns. These results highlight how deep learning algorithms may improve systems for categorizing music genres, with implications for various music-related applications and user interfaces. Up to this point, 92.7% of the GTZAN dataset's correctness has been achieved on the GTZAN dataset and 91.6% on the ISMIR2004 Ballroom dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"457-467"},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141741079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification DeepCon：为结直肠癌分类释放分而治之深度学习的力量

IEEE Open Journal of the Computer Society Pub Date : 2024-07-16 DOI: 10.1109/OJCS.2024.3428970

Suhaib Chughtai;Zakaria Senousy;Ahmed Mahany;Nouh Sabri Elmitwally;Khalid N. Ismail;Mohamed Medhat Gaber;Mohammed M. Abdelsamea

{"title":"DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification","authors":"Suhaib Chughtai;Zakaria Senousy;Ahmed Mahany;Nouh Sabri Elmitwally;Khalid N. Ismail;Mohamed Medhat Gaber;Mohammed M. Abdelsamea","doi":"10.1109/OJCS.2024.3428970","DOIUrl":"10.1109/OJCS.2024.3428970","url":null,"abstract":"Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this article, we introduce a novel deep learning model, termed \u0000<italic>DeepCon</i>\u0000 which incorporates the divide-and-conquer principle into the classification task. \u0000<italic>DeepCon</i>\u0000 has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that \u0000<italic>DeepCon</i>\u0000 when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"380-388"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Taxonomy for Python Vulnerabilities Python 漏洞分类标准

IEEE Open Journal of the Computer Society Pub Date : 2024-07-03 DOI: 10.1109/OJCS.2024.3422686

Frédéric C. G. Bogaerts;Naghmeh Ivaki;José Fonseca

{"title":"A Taxonomy for Python Vulnerabilities","authors":"Frédéric C. G. Bogaerts;Naghmeh Ivaki;José Fonseca","doi":"10.1109/OJCS.2024.3422686","DOIUrl":"10.1109/OJCS.2024.3422686","url":null,"abstract":"Python is one of the most widely adopted programming languages, with applications from web development to data science and machine learning. Despite its popularity, Python is susceptible to vulnerabilities compromising the systems that rely on it. To effectively address these challenges, developers, researchers, and security teams need to identify, analyze, and mitigate risks in Python code, but this is not an easy task due to the scattered, incomplete, and non-actionable nature of existing vulnerability data. This article introduces a comprehensive dataset comprising 1026 publicly disclosed Python vulnerabilities sourced from various repositories. These vulnerabilities are meticulously classified using widely recognized frameworks, such as Orthogonal Defect Classification (ODC), Common Weakness Enumeration (CWE), and Open Web Application Security Project (OWASP) Top 10. Our dataset is accompanied by patched and vulnerable code samples (some crafted with the help of AI), enhancing its utility for developers, researchers, and security teams. In addition, a user-friendly website was developed to allow its interactive exploration and facilitate new contributions from the community. Access to this dataset will foster the development and testing of safer Python applications. The resulting dataset is also analyzed, looking for trends and patterns in the occurrence of Python vulnerabilities, with the aim of raising awareness of Python security and providing practical, actionable guidance to assist developers, researchers, and security teams in bolstering their practices. This includes insights into the types of vulnerabilities they should focus on, the most exploited categories, and the common errors that programmers tend to make while coding that can lead to vulnerabilities.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"368-379"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-Lambertian Surfaces and Their Challenges for Visual SLAM 非朗伯表面及其对视觉 SLAM 的挑战

IEEE Open Journal of the Computer Society Pub Date : 2024-06-27 DOI: 10.1109/OJCS.2024.3419832

Sara Pyykölä;Niclas Joswig;Laura Ruotsalainen

引用次数: 0

The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection 用于棉花害虫检测的视觉变压器和声学传感器的威力

IEEE Open Journal of the Computer Society Pub Date : 2024-06-25 DOI: 10.1109/OJCS.2024.3419027

Remya S;Anjali T;Abhishek S;Somula Ramasubbareddy;Yongyun Cho

{"title":"The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection","authors":"Remya S;Anjali T;Abhishek S;Somula Ramasubbareddy;Yongyun Cho","doi":"10.1109/OJCS.2024.3419027","DOIUrl":"10.1109/OJCS.2024.3419027","url":null,"abstract":"Whitefly infestations have posed a severe threats to cotton crops in recent years, affecting farmers globally. These little insects consume food on cotton plants, causing leaf damage and lower crop yields. In response to this agricultural dilemma, we developed a novel method for detecting whitefly infestations in cotton fields. To improve pest detection accuracy, we use the combined efficiency of visual transformers and low-cost acoustic sensors. We train the vision transformer with a large dataset of cotton fields with and without whitefly infestations. Our studies yielded encouraging results, with the vision transformer obtaining an amazing 99% accuracy. Surprisingly, this high degree of accuracy is reached after only 10-20 training epochs, outperforming benchmark approaches, which normally give accuracies ranging from 80% to 90%. These outcomes underline the cost-effective potential of the vision transformer in detecting whitefly attacks on cotton crops. Moreover, the successful integration of acoustic sensors and vision transformers opens doors for further research and advancements in the domain of cotton pest detection, promising more robust and efficient solutions for farmers facing the challenges of whitefly infestations.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"356-367"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ECC-PDGPP: ECC-Based Parallel Dependency RFID-Grouping-Proof Protocol Using Zero-Knowledge Property in the Internet of Things Environment ECC-PDGPP：在物联网环境中使用零知识属性的基于 ECC 的并行依赖性 RFID-Grouping-Proof 协议

IEEE Open Journal of the Computer Society Pub Date : 2024-06-03 DOI: 10.1109/OJCS.2024.3406142

Suman Majumder;Sangram Ray;Dipanwita Sadhukhan;Mou Dasgupta;Ashok Kumar Das;Youngho Park

{"title":"ECC-PDGPP: ECC-Based Parallel Dependency RFID-Grouping-Proof Protocol Using Zero-Knowledge Property in the Internet of Things Environment","authors":"Suman Majumder;Sangram Ray;Dipanwita Sadhukhan;Mou Dasgupta;Ashok Kumar Das;Youngho Park","doi":"10.1109/OJCS.2024.3406142","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3406142","url":null,"abstract":"Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"329-342"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization 基于修正艾奇逊-艾特肯函数贝叶斯优化的组合爆炸决策树多规则挖掘算法

IEEE Open Journal of the Computer Society Pub Date : 2024-04-30 DOI: 10.1109/OJCS.2024.3394928

Yuto Omae;Masaya Mori;Yohei Kakimoto

{"title":"Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization","authors":"Yuto Omae;Masaya Mori;Yohei Kakimoto","doi":"10.1109/OJCS.2024.3394928","DOIUrl":"10.1109/OJCS.2024.3394928","url":null,"abstract":"Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if–then rules. However, as decision trees are constructed by an algorithm that achieves clear classification with minimum necessary rules, the trees possess the drawback of extracting only minimum rules, even when various latent rules exist in data. Approaches that construct multiple trees using randomly selected feature subsets do exist. However, the number of trees that can be constructed remains at the same scale because the number of feature subsets is a combinatorial explosion. Additionally, when multiple trees are constructed, numerous rules are generated, of which several are untrustworthy and/or highly similar. Therefore, we propose “MAABO-MT” and “GS-MRM” algorithms that strategically construct trees with high estimation performance among all possible trees with small computational complexity and extract only reliable and non-similar rules, respectively. Experiments are conducted using several open datasets to analyze the effectiveness of the proposed method. The results confirm that MAABO-MT can discover reliable rules at a lower computational cost than other methods that rely on randomness. Furthermore, the proposed method is confirmed to provide deeper insights than single decision trees commonly used in previous studies. Therefore, MAABO-MT and GS-MRM can efficiently extract rules from combinatorially exploded decision trees.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"215-226"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Affective Computing and the Road to an Emotionally Intelligent Metaverse 情感计算与情感智能元宇宙之路

IEEE Open Journal of the Computer Society Pub Date : 2024-04-18 DOI: 10.1109/OJCS.2024.3389462

Farrukh Pervez;Moazzam Shoukat;Muhammad Usama;Moid Sandhu;Siddique Latif;Junaid Qadir

{"title":"Affective Computing and the Road to an Emotionally Intelligent Metaverse","authors":"Farrukh Pervez;Moazzam Shoukat;Muhammad Usama;Moid Sandhu;Siddique Latif;Junaid Qadir","doi":"10.1109/OJCS.2024.3389462","DOIUrl":"10.1109/OJCS.2024.3389462","url":null,"abstract":"The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and context-aware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"195-214"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Neighborhood Contexts in Explainable AI: An Explanandum-Based Evaluation 可解释人工智能中的最佳邻域语境：基于解释备忘录的评估

IEEE Open Journal of the Computer Society Pub Date : 2024-04-18 DOI: 10.1109/OJCS.2024.3389781

Urja Pawar;Donna O'Shea;Ruairi O'Reilly;Maebh Costello;Christian Beder

{"title":"Optimal Neighborhood Contexts in Explainable AI: An Explanandum-Based Evaluation","authors":"Urja Pawar;Donna O'Shea;Ruairi O'Reilly;Maebh Costello;Christian Beder","doi":"10.1109/OJCS.2024.3389781","DOIUrl":"10.1109/OJCS.2024.3389781","url":null,"abstract":"Over the years, several frameworks have been proposed in the domain of Explainable AI (XAI), however their practical applicability and utility need to be clarified. The neighbourhood contexts are shown to significantly impact the explanations generated by XAI frameworks, thus directly affecting their utility in addressing specific questions, or “explananda”. This work introduces a methodology that use a comprehensive range of neighbourhood contexts to evaluate and enhance the utility of specific XAI techniques, particularly Feature Importance and CounterFactuals. In this evaluation, two explananda are targeted. The first one examines whether features' collection should be halted as per the AI model based on the sufficiency of the current set of information. Here, the information refers to the features present in the data used to train the AI-based system. The second one explores what is the most effective information (features) that should be collected next to ensure that the AI outputs the same classification as it would have generated with all the information present. These questions serve as a platform to demonstrate our methodology's ability to assess the impact of customised neighbourhood contexts on the utility of XAI.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"181-194"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks 基于未来车载网络综合传感与通信的智能路径损耗预测方法

IEEE Open Journal of the Computer Society Pub Date : 2024-04-09 DOI: 10.1109/OJCS.2024.3386733

Zixiang Wei;Bomin Mao;Hongzhi Guo;Yijie Xun;Jiajia Liu;Nei Kato

{"title":"An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks","authors":"Zixiang Wei;Bomin Mao;Hongzhi Guo;Yijie Xun;Jiajia Liu;Nei Kato","doi":"10.1109/OJCS.2024.3386733","DOIUrl":"10.1109/OJCS.2024.3386733","url":null,"abstract":"The developments of communication technologies, Internet of Things (IoT), and Artificial Intelligence (AI) have significantly accelerated the advancement of Intelligent Transportation Systems (ITS) and Autonomous Driving (AD) in recent years. The exchange of sensed information by widely deployed radars, cameras, and other sensors on vehicles and roadside infrastructure can improve the traffic awareness of drivers and pedestrians. However, wireless data transmission in vehicular networks is challenged by highly dynamic path loss due to utilized frequency bands, weather conditions, traffic overheads, and geographical conditions. In this paper, we propose an Integrated Sensing and Communication System (ISAC) based path loss prediction approach to improve the knowledge of wireless data transmissions in vehicular networks, which utilizes multi-modal data collected by millimeter-wave (mmWave) radars, laser radars, and cameras to forecast the end-to-end path loss distribution. By leveraging a generative adversarial network for parameter initialization coupled with fine-tuning through supervised learning, the model's accuracy can be significantly improved. To increase the model's scalability, the effects of weather conditions, geographical conditions, traffic overheads, and frequency bands are all analyzed. According to the simulation results, our model achieves excellent accuracy with Mean Squared Error (MSE) of the predicted path loss distribution below \u0000<inline-formula><tex-math>$3e^{-3}$</tex-math></inline-formula>\u0000 across five different scenarios.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"170-180"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0