2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)最新文献

Customized CNN with Adam and Nadam Optimizers for Emotion Recognition using Facial Expressions 定制CNN与亚当和那达姆优化器使用面部表情的情绪识别

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134002

Kuppusamy P, Raga Siri P, H. P, Dhanyasri M, C. Iwendi

{"title":"Customized CNN with Adam and Nadam Optimizers for Emotion Recognition using Facial Expressions","authors":"Kuppusamy P, Raga Siri P, H. P, Dhanyasri M, C. Iwendi","doi":"10.1109/WiSPNET57748.2023.10134002","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134002","url":null,"abstract":"People communicate using one of the communication types of facial expressions to express their emotions. Human feelings are detected through facial expressions to interpret their present state of mood. It stimulates researchers to work in the field of emotion recognition. The design of deep learning models is essential to interpret the human current mind state by capturing the pattern of the facial gesture through their facial expressions. This study proposed a customized Convolutional Neural Network (CNN) with various optimizers Adaptive Moment Estimation (Adam) and Nesterov-accelerated Adaptive Moment Estimation (Nadam) to improve emotion recognition using the dataset FER-2013. The customized proposed model is designed by varying the number of convolution layers, filters, filter sizes, and optimizers. The emotions are recognized using softmax activation in the output layer. The experimental results have proved that the proposed model classified the facial expressions with accuracy of 0.841, 0.826 using Nadam and Adam optimizers respectively.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Quad Port Pattern Reconfigurable Antenna for Indoor Applications 室内应用四端口模式可重构天线

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134417

Deepa Thangarasu, T. Rao, S. Palaniswamy

引用次数: 0

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134007

Vanila Sildas, Preman Venkatesh Chandraman, Srinivasan Raj

引用次数: 0

Study of Characteristics and Applications of Microwave Photonic Radar 微波光子雷达的特性及应用研究

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134140

Adrish Sahu, Gaurav Singh Chandrabhan, Viswadruth Akkaraju, R. T.

引用次数: 0

Enhanced Deep Convolutional Neural Network for Identifying and Classification of Silicon Wafer Faults in IC Fabrication Industries 基于深度卷积神经网络的集成电路晶圆故障识别与分类

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10133996

G. Ram, M. Subbarao, D. R. Varma, A. S. Krishna

{"title":"Enhanced Deep Convolutional Neural Network for Identifying and Classification of Silicon Wafer Faults in IC Fabrication Industries","authors":"G. Ram, M. Subbarao, D. R. Varma, A. S. Krishna","doi":"10.1109/WiSPNET57748.2023.10133996","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10133996","url":null,"abstract":"This paper presents the detection and classification of various manufacturing defects on wafer maps using an enhanced deep convolutional neural network (DCNN). Wafers are tiny discs of semiconducting material, often silicon, that form the basis of integrated circuits. Die-separated integrated circuits (ICs) are produced on each wafer. Automated inspection machines evaluate the functionality of ICs on wafers. On a wafer map, the regional pattern of the passing and failing dies might identify the specific production faults. Using techniques of deep learning, the defect patterns on wafers may be efficiently classified, making it possible to rapidly identify production defects, hence enabling early manufacturing process correction and minimising loss. Resampling is performed in order to resolve the data imbalance problem prior to the training modality. Further performance analysis is carried out by incorporating various optimizers to train the model. The simulation results depicted that the proposed DCNN outperforms the conventional CNN.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123976868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gradient Adaptive Planar Prediction for High-Fidelity Images 高保真图像的梯度自适应平面预测

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134321

Ravi Uyyala, M. Subramaniam

引用次数: 0

Understanding DeepFool Adversarial Attack and Defense with Skater Interpretations 理解DeepFool对抗性攻击和防御与溜冰者的解释

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134485

Dhivyashri Ramesh, Ishwarya Sriram, Kavya Sridhar, Snofy D. Dunston, M. V

{"title":"Understanding DeepFool Adversarial Attack and Defense with Skater Interpretations","authors":"Dhivyashri Ramesh, Ishwarya Sriram, Kavya Sridhar, Snofy D. Dunston, M. V","doi":"10.1109/WiSPNET57748.2023.10134485","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134485","url":null,"abstract":"With the incorporation of artificial intelligence in businesses, particularly features like computer vision, it has become increasingly important to ensure the robustness of the models being used. A popular technique used to exploit machine learning models is an adversarial attack. Adversarial attacks mis-lead a predictive model by providing it with perturbed input. In the context of computer vision, it involves creating perturbations in an image to deceive a model. One such adversarial attack is the DeepFool attack, which aims to create the most minimal perturbations to an image to deceive the model. These attacks can also affect the way in which interpretations are made. In this paper, we analyze the DeepFool attack and its countermeasures on the ResNet-50 model running on the NIH malarial dataset. To assess the efficiency of the attack and subsequent adversarial training, we have used accuracy and loss. The nature and impact of the attack and adversarial training are analysed using skater, a model interpretation framework. The variations in the interpretations when adversarial attacks are in place are also analysed.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132635224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Simulation of a Novel Cell Interaction Based Square Calculator in Quantum-Dot Cellular Automata 量子点元胞自动机中基于细胞相互作用的方形计算器的设计与仿真

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134190

Dipin Thomas, V. S. Solomi

引用次数: 0

Ensemble Learning Solution for the Aspect-based Sentimental Analysis on IMDB reviews 基于方面的IMDB评论情感分析集成学习解决方案

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134144

Kakavakam Jaswanth Sai, S. Chakravarthi, S. Sountharrajan, E. Suganya

{"title":"Ensemble Learning Solution for the Aspect-based Sentimental Analysis on IMDB reviews","authors":"Kakavakam Jaswanth Sai, S. Chakravarthi, S. Sountharrajan, E. Suganya","doi":"10.1109/WiSPNET57748.2023.10134144","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134144","url":null,"abstract":"Nowadays, social media significantly influences how people form opinions about any type of business, politics, commerce, etc. based on user ratings. These reviews were examined using the field of sentiment analysis. This is a crucial component since well-designed and carried out sentiment assessments may lead to better and more accurate estimates in both business and politics. Sentiment analysis is skilled at overcoming a variety of difficulties, including issues with accuracy, problems with binary classification, problems with polarity change and data scarcity. There have been several approaches proposed and developed for this, but none of them have been effective in consistently extracting sentiment analysis. We reviewed the traditional lexicon-based method and then we developed an ensemble model employing machine learning algorithms that outperformed the lexicon-based approach by 89 percent. Additionally, we have shown through a comparison study why the suggested model is the most effective. The stacking classifier ensemble strategy that we utilized in this case allowed us to boost classification accuracy by 1% while utilizing a variety of well-known machine learning algorithms.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127930179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Realization of Electronically Controllable, Wide bandwidth Instrumentation Amplifier using single DVCCTA 单DVCCTA实现电控宽频带仪表放大器

2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) Pub Date : 2023-03-29 DOI: 10.1109/WiSPNET57748.2023.10134111

Harika Pamu, Kishore Kumar Puli, K. Gurrala

{"title":"Realization of Electronically Controllable, Wide bandwidth Instrumentation Amplifier using single DVCCTA","authors":"Harika Pamu, Kishore Kumar Puli, K. Gurrala","doi":"10.1109/WiSPNET57748.2023.10134111","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134111","url":null,"abstract":"In this research paper, a simple Instrumentation Amplifier (IA) which can operate in voltage and transadmittance modes employing a sole Differential Voltage Current Conveyor Transconductance Amplifier (DVCCTA) with only one grounded resistor is presented. Both the modes are concurrently given within the same circuit topology and also avails the feature of IC integration. In addition, the proposed topologies provide a wide range of differential gain bandwidth and CMRR bandwidth of about (1.05 GHz, 17.286 MHz) for transadmittance mode, (1.02GHz, 17.286 MHz) for voltage mode respectively. Furthermore, the proposed design has an appealing feature of electronic tuning of differential gains via bias current (IB). CMOS based DVCCTA is used to authenticate the workableness of the proposed designs through PSPICE with 0.18 μm TSMC CMOS technology parameter. The performance of the proposed topologies is evaluated in terms of non-ideal analysis, Monte Carlo simulations and temperature dependent variations. The simulated responses correlate with the theoretical prediction.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116676423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0