Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729364
Alireza Makki, Alireza Hadi, Bahram Tarvirdizadeh, M. Teimouri
In this article, the vision problem in a robotic application is under focus to handle the grasping of objects based on a new method. Converting an object into primitive objects is assumed to be done in the first step of the vision scenario. The second step, which is the main contribution of this paper, is classifying a primitive object and determining its position, orientation, and dimensions. In this way, the voxel data with three Cartesian channels of a primitive object is considered the input of a convolutional neural network that extracts the required parameters. A virtual camera in the simulation tool (Gazebo) is used to prepare the necessary dataset for training the neural network. Although the use of voxel data with Cartesian channels increases the volume of input data and slows down the processing speed, it is shown in this study that it effectively improves the accuracy of the network in estimating the parameters of primitive objects. Based on the provided virtual dataset, the mean errors when using Cartesian channels are decreased 81%, −33%, and 53% for the position, orientation, and dimensions, respectively, compared to binary voxel data. In the same comparison, these errors are −7%, 80%, and 55% lower than RGB data.
{"title":"POPDNet: Primitive Object Pose Detection Network Based on Voxel Data with Three Cartesian Channels","authors":"Alireza Makki, Alireza Hadi, Bahram Tarvirdizadeh, M. Teimouri","doi":"10.1109/ICSPIS54653.2021.9729364","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729364","url":null,"abstract":"In this article, the vision problem in a robotic application is under focus to handle the grasping of objects based on a new method. Converting an object into primitive objects is assumed to be done in the first step of the vision scenario. The second step, which is the main contribution of this paper, is classifying a primitive object and determining its position, orientation, and dimensions. In this way, the voxel data with three Cartesian channels of a primitive object is considered the input of a convolutional neural network that extracts the required parameters. A virtual camera in the simulation tool (Gazebo) is used to prepare the necessary dataset for training the neural network. Although the use of voxel data with Cartesian channels increases the volume of input data and slows down the processing speed, it is shown in this study that it effectively improves the accuracy of the network in estimating the parameters of primitive objects. Based on the provided virtual dataset, the mean errors when using Cartesian channels are decreased 81%, −33%, and 53% for the position, orientation, and dimensions, respectively, compared to binary voxel data. In the same comparison, these errors are −7%, 80%, and 55% lower than RGB data.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121842680","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729353
Mohammad Hajipour, M. Akhaee, Ramin Toosi
Automatic Speaker Verification (ASV) is a biometric authentication system identifying a person based on the voice presented to a system. Nowadays, due to the widespread use of these systems, various attacks are carried out on them. These attacks are in four different formats, which are impersonation, speech synthesis, voice conversion and replay attack. One of the most commonly used attacks is replay attack due to its simplicity. The purpose of this study is to provide a countermeasure system against replay attacks. We found that the effect of noises generated by different recorders and playback devices on the spoof samples can be used as a criterion for attack detection. So this study analyzes the silent parts of the speech signal that include the noises of various recording and playback devices. Also due to the proper operation of deep convolutional neural networks in classification applications, we propose an ensemble classifier based on end to end neural networks architecture and residual structures to accurately distinguish spoof utterances from genuine ones. We have decreased the t-DCF metric on ASVspoof2019 database by almost 16% compared to similar models that have processed on full speech signals.
{"title":"Listening to Sounds of Silence for Audio replay attack detection","authors":"Mohammad Hajipour, M. Akhaee, Ramin Toosi","doi":"10.1109/ICSPIS54653.2021.9729353","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729353","url":null,"abstract":"Automatic Speaker Verification (ASV) is a biometric authentication system identifying a person based on the voice presented to a system. Nowadays, due to the widespread use of these systems, various attacks are carried out on them. These attacks are in four different formats, which are impersonation, speech synthesis, voice conversion and replay attack. One of the most commonly used attacks is replay attack due to its simplicity. The purpose of this study is to provide a countermeasure system against replay attacks. We found that the effect of noises generated by different recorders and playback devices on the spoof samples can be used as a criterion for attack detection. So this study analyzes the silent parts of the speech signal that include the noises of various recording and playback devices. Also due to the proper operation of deep convolutional neural networks in classification applications, we propose an ensemble classifier based on end to end neural networks architecture and residual structures to accurately distinguish spoof utterances from genuine ones. We have decreased the t-DCF metric on ASVspoof2019 database by almost 16% compared to similar models that have processed on full speech signals.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531834","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729366
Vahid Hajihashemi, Abdorreza Alavigharahbagh, A. Bastanfard, Hamid Esmaeili Najafabadi, João Manuel R. S. Tavares
Holography is a 3D capturing and displaying system. Many formats have been suggested to store holographic images with the highest quality and minimum file size. Here, we suggest combining two AV1 codecs to make a secondary error image and use it in a linear regression block to compensate for the main AV1 compression error. Since the phase part is the most challenging part of holograms, the proposed method addresses the compression problem in phase. The obtained results reveal that the proposed method can outperform the state-of-the-art codecs in terms of PSNR and SSIM criteria. Besides, comparing BD-PSNR and BD-Rate results with usual AV1, confirms the proposed method has an average about 5.04dB, which is −22.1% better Object plane performance, and 4.57 dB, which is −20.66% better in Holo plane performance, in terms of BDPSNR and BD-Rate, respectively.
{"title":"Extending AV1 Codec to Enhance Quality in Phase Compression of Digital Holograms in Object and Hologram Planes","authors":"Vahid Hajihashemi, Abdorreza Alavigharahbagh, A. Bastanfard, Hamid Esmaeili Najafabadi, João Manuel R. S. Tavares","doi":"10.1109/ICSPIS54653.2021.9729366","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729366","url":null,"abstract":"Holography is a 3D capturing and displaying system. Many formats have been suggested to store holographic images with the highest quality and minimum file size. Here, we suggest combining two AV1 codecs to make a secondary error image and use it in a linear regression block to compensate for the main AV1 compression error. Since the phase part is the most challenging part of holograms, the proposed method addresses the compression problem in phase. The obtained results reveal that the proposed method can outperform the state-of-the-art codecs in terms of PSNR and SSIM criteria. Besides, comparing BD-PSNR and BD-Rate results with usual AV1, confirms the proposed method has an average about 5.04dB, which is −22.1% better Object plane performance, and 4.57 dB, which is −20.66% better in Holo plane performance, in terms of BDPSNR and BD-Rate, respectively.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316881","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729343
Hannaneh Mahdavi, S. Rahbarpour, S. Hosseini-Golgoo, H. Jamaati
The response signals of temperature modulated gas sensors contain essential information about measured target gas that must be separated from other correlated, redundant, or noisy data. This issue becomes more critical when variations in environmental factors such as relative humidity of target gas or background odors affect the sensor response. Conductance values of two electronic noses based on a single TGS-2602 and a single FIS SP-53B sensors to four gases and clean air at a wide range of relative humidity levels were measured for analyzing the response features. The role of each feature and increasing the number of features in the accuracy of an SVM classifier are investigated. A method is proposed based on removing non-informative features and compared to four conventional feature selection techniques. It is shown that our proposed scheme with a simple SVM classifier results in 96.7% detection accuracy for TGS-2602 and 98.8% for FIS SP-53B, which is up to the accuracy value of common or advanced methods of selecting features. It is concluded that employing feature selection techniques is more beneficial for the TGS-2602 dataset, which had more destructive features than FIS SP-53B. In conclusion, when working with an E-Nose dataset, it is first necessary to explore the important features to find out whether feature selection is required or not, and if needed, which feature selection method provides the best accuracy.
{"title":"Exploring Informative Response Features of Two Temperature Modulated Gas Sensors at a Wide Range of Relative Humidity","authors":"Hannaneh Mahdavi, S. Rahbarpour, S. Hosseini-Golgoo, H. Jamaati","doi":"10.1109/ICSPIS54653.2021.9729343","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729343","url":null,"abstract":"The response signals of temperature modulated gas sensors contain essential information about measured target gas that must be separated from other correlated, redundant, or noisy data. This issue becomes more critical when variations in environmental factors such as relative humidity of target gas or background odors affect the sensor response. Conductance values of two electronic noses based on a single TGS-2602 and a single FIS SP-53B sensors to four gases and clean air at a wide range of relative humidity levels were measured for analyzing the response features. The role of each feature and increasing the number of features in the accuracy of an SVM classifier are investigated. A method is proposed based on removing non-informative features and compared to four conventional feature selection techniques. It is shown that our proposed scheme with a simple SVM classifier results in 96.7% detection accuracy for TGS-2602 and 98.8% for FIS SP-53B, which is up to the accuracy value of common or advanced methods of selecting features. It is concluded that employing feature selection techniques is more beneficial for the TGS-2602 dataset, which had more destructive features than FIS SP-53B. In conclusion, when working with an E-Nose dataset, it is first necessary to explore the important features to find out whether feature selection is required or not, and if needed, which feature selection method provides the best accuracy.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117285426","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729391
Amir Hassanpour, Habib Izadkhah, A. Isazadeh
Topological data analysis (TDA) is a novel and rapidly growing area of modern data science that uses topological, geometric, and algebraic tools to extract structural features from very complex and large-scale data that are usually incomplete and noisy. The primary motivation for studying this method was to study the shape of data, which has been connected to branches of pure mathematics such as homology, cohomology, and algebraic topology. In this method, the topological space obtained from cloud data can give it an interpretation of distance, continuity, and connectedness so patterns and relationships between the data are discovered quickly. In other words, using this method, the original information can be obtained from the sample or accidental information that was lost or messed up during sampling. Persistent homology is One of the essential tools of TDA. In this paper, after introducing the necessary mathematical concepts, through computing persistent homology and extracting appropriate features, we provide a new dataset, and we then develop a deep learning architecture to predict the protein secondary structure from the constructed dataset. The accuracy of the proposed method is at least 5% higher than the accuracy of the previous methods.
{"title":"Protein Secondary Structure Prediction using Topological Data Analysis","authors":"Amir Hassanpour, Habib Izadkhah, A. Isazadeh","doi":"10.1109/ICSPIS54653.2021.9729391","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729391","url":null,"abstract":"Topological data analysis (TDA) is a novel and rapidly growing area of modern data science that uses topological, geometric, and algebraic tools to extract structural features from very complex and large-scale data that are usually incomplete and noisy. The primary motivation for studying this method was to study the shape of data, which has been connected to branches of pure mathematics such as homology, cohomology, and algebraic topology. In this method, the topological space obtained from cloud data can give it an interpretation of distance, continuity, and connectedness so patterns and relationships between the data are discovered quickly. In other words, using this method, the original information can be obtained from the sample or accidental information that was lost or messed up during sampling. Persistent homology is One of the essential tools of TDA. In this paper, after introducing the necessary mathematical concepts, through computing persistent homology and extracting appropriate features, we provide a new dataset, and we then develop a deep learning architecture to predict the protein secondary structure from the constructed dataset. The accuracy of the proposed method is at least 5% higher than the accuracy of the previous methods.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133221491","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729350
Mahdi Abdollah Chalaki, Daniyal Maroufi, M. Robati, Mohammad Javad Karimi, A. Sadighi
Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.
{"title":"An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods","authors":"Mahdi Abdollah Chalaki, Daniyal Maroufi, M. Robati, Mohammad Javad Karimi, A. Sadighi","doi":"10.1109/ICSPIS54653.2021.9729350","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729350","url":null,"abstract":"Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487114","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729339
Mohammad Ghanatian, M. Yazdi, M. T. Masouleh
This paper aims to smoothen the movements of an under-constrained cable-suspended parallel robot which carries a camera for video capturing purposes, especially for video capturing of football games. This goal is achieved by means of an accurate while simple PID controller optimized by the Simulated Annealing algorithm and implemented on the joint space. Moreover, a planning strategy is considered for the joint space trajectory of the robot which guarantees the zero jerk, acceleration, and velocity at the start and the end of motions. On this regard, a septic function for each joint of the robot is considered and the corresponding boundary conditions are applied to the function to make the end-effector movements less oscillatory. This method is implemented and tested on an experimental setup while the end-effector oscillation data is recorded using an IMU sensor attached to the end-effector of the robot. Applying frequency analysis on the oscillatory data of the end-effector reveals that this simple method, on average, resulted in a 33.8% reduction in the average amplitude of the end-effector oscillations. Moreover, the maximum joint space error was decreased by 76.68% when using septic joint profile compared to the ordinary linear Cartesian trajectory planning approach. Upon applying the proposed strategy, the error of the controller has been reduced by 92.26% with respect to the previous research performed on this experimental setup. Without requiring any knowledge on the dynamic model of the robot or the natural frequencies of the end-effector or using any complex controller, this method significantly increased the smoothness and accuracy of the robot movements. The proposed method can be regarded as a definitive asset when this robot is used for video capturing purposes.
{"title":"Experimental Study on Reducing the Oscillations of a Cable-Suspended Parallel Robot for Video Capturing Purposes using Simulated Annealing and Path Planning","authors":"Mohammad Ghanatian, M. Yazdi, M. T. Masouleh","doi":"10.1109/ICSPIS54653.2021.9729339","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729339","url":null,"abstract":"This paper aims to smoothen the movements of an under-constrained cable-suspended parallel robot which carries a camera for video capturing purposes, especially for video capturing of football games. This goal is achieved by means of an accurate while simple PID controller optimized by the Simulated Annealing algorithm and implemented on the joint space. Moreover, a planning strategy is considered for the joint space trajectory of the robot which guarantees the zero jerk, acceleration, and velocity at the start and the end of motions. On this regard, a septic function for each joint of the robot is considered and the corresponding boundary conditions are applied to the function to make the end-effector movements less oscillatory. This method is implemented and tested on an experimental setup while the end-effector oscillation data is recorded using an IMU sensor attached to the end-effector of the robot. Applying frequency analysis on the oscillatory data of the end-effector reveals that this simple method, on average, resulted in a 33.8% reduction in the average amplitude of the end-effector oscillations. Moreover, the maximum joint space error was decreased by 76.68% when using septic joint profile compared to the ordinary linear Cartesian trajectory planning approach. Upon applying the proposed strategy, the error of the controller has been reduced by 92.26% with respect to the previous research performed on this experimental setup. Without requiring any knowledge on the dynamic model of the robot or the natural frequencies of the end-effector or using any complex controller, this method significantly increased the smoothness and accuracy of the robot movements. The proposed method can be regarded as a definitive asset when this robot is used for video capturing purposes.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126529507","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729371
K. Safarihamid, A. Pourafzal, A. Fereidunian
In this paper, the problem of entropy-based classification of time-series into stochastic, chaotic, and periodic is addressed, followed by proposing an alternative joint-entropy approach to time series classification. These data-driven methods describe the behavior of a signal, using the association of the entropy of a time-series with emergence and self-organization, as complex systems characteristics. First, we deduce that certain groups of entropies, namely Fuzzy entropy, and Distribution entropy, share more similarities with emergence, while permutation and dispersion entropies could be associated with self-organization. Then, we utilize these resemblances to propose a joint-entropy alternative approach, in which one of the specific entropies is presented for each characteristic. Further, in simulations, we evaluated the performance of our proposed approach, comparing with single entropy methods, using different classifiers and decision boundaries. The results reveal an excellent performance of 98% accuracy for simultaneous utilization of the Distribution and Permutation entropies as the input features of Random Forest classifier, while this value is at best 89% for when only a single entropy is fed to the classifier.
{"title":"A Joint-Entropy Approach To Time-series Classification","authors":"K. Safarihamid, A. Pourafzal, A. Fereidunian","doi":"10.1109/ICSPIS54653.2021.9729371","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729371","url":null,"abstract":"In this paper, the problem of entropy-based classification of time-series into stochastic, chaotic, and periodic is addressed, followed by proposing an alternative joint-entropy approach to time series classification. These data-driven methods describe the behavior of a signal, using the association of the entropy of a time-series with emergence and self-organization, as complex systems characteristics. First, we deduce that certain groups of entropies, namely Fuzzy entropy, and Distribution entropy, share more similarities with emergence, while permutation and dispersion entropies could be associated with self-organization. Then, we utilize these resemblances to propose a joint-entropy alternative approach, in which one of the specific entropies is presented for each characteristic. Further, in simulations, we evaluated the performance of our proposed approach, comparing with single entropy methods, using different classifiers and decision boundaries. The results reveal an excellent performance of 98% accuracy for simultaneous utilization of the Distribution and Permutation entropies as the input features of Random Forest classifier, while this value is at best 89% for when only a single entropy is fed to the classifier.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886567","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729352
Setareh Mokhtari, Hadi Shakibian
In this paper, a new distributed boosting technique has been proposed based on particle swarm optimization (PSO) in order to efficiently perform the regression modeling in wireless sensor networks (WSNs). The proposed algorithm learns the network regressor in two stages: (i) the clusters regressors are learned using distributed PSO, and (ii) the accuracy of the obtained models are improved through a boosting technique. The results on real dataset show that the proposed algorithm could obtain high accurate model with completely acceptable energy consumption in comparison to other distributed algorithms.
{"title":"Asynchronous PSO for Distributed Optimization in Clustered Sensor Networks","authors":"Setareh Mokhtari, Hadi Shakibian","doi":"10.1109/ICSPIS54653.2021.9729352","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729352","url":null,"abstract":"In this paper, a new distributed boosting technique has been proposed based on particle swarm optimization (PSO) in order to efficiently perform the regression modeling in wireless sensor networks (WSNs). The proposed algorithm learns the network regressor in two stages: (i) the clusters regressors are learned using distributed PSO, and (ii) the accuracy of the obtained models are improved through a boosting technique. The results on real dataset show that the proposed algorithm could obtain high accurate model with completely acceptable energy consumption in comparison to other distributed algorithms.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133772333","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}
Pub Date : 2021-12-29DOI: 10.1109/ICSPIS54653.2021.9729388
M. M. Esfahani, H. Sadati
Brain-Computer-Interface systems were invented in the last decade to record brain signals and then control a system that behaves and conveys with a biosignal recording device and the brain. Its major objective is to aid individuals who suffer from behavioral infirmity. The focus of this research is to analyze the cortical surface of the brain's hemodynamic response using functional near-infrared spectroscopy signals (fNIRS). It is utilized in a variety of cognitive neuroscience and behavioral rehabilitation treatments. Additionally, it was applied to classify thirty participants who volunteered to do a task divided into three classes. The primary task is to classify multi-class fNIRS signals using various classification methods and then compare the results. We utilized classification methods for each of the 30 subjects, followed by the voting and stacking procedures as part of an ensemble learning method. The averaged results for all subjects reached 64.813 percent, while ensemble learning using the voting method reached 66.416 percent. Following that, ensemble learning using the stacking method combined with the ANFIS kernel reached 60.6616 percent. Finally, the findings suggest that it may improve accuracy and reduce standard deviation depending on the Ensemble Learning approach used. It asserts that when the variance of the predictions was reduced, the classification model produced better results.
{"title":"fNIRS Signals Classification with Ensemble Learning and Adaptive Neuro-Fuzzy Inference System","authors":"M. M. Esfahani, H. Sadati","doi":"10.1109/ICSPIS54653.2021.9729388","DOIUrl":"https://doi.org/10.1109/ICSPIS54653.2021.9729388","url":null,"abstract":"Brain-Computer-Interface systems were invented in the last decade to record brain signals and then control a system that behaves and conveys with a biosignal recording device and the brain. Its major objective is to aid individuals who suffer from behavioral infirmity. The focus of this research is to analyze the cortical surface of the brain's hemodynamic response using functional near-infrared spectroscopy signals (fNIRS). It is utilized in a variety of cognitive neuroscience and behavioral rehabilitation treatments. Additionally, it was applied to classify thirty participants who volunteered to do a task divided into three classes. The primary task is to classify multi-class fNIRS signals using various classification methods and then compare the results. We utilized classification methods for each of the 30 subjects, followed by the voting and stacking procedures as part of an ensemble learning method. The averaged results for all subjects reached 64.813 percent, while ensemble learning using the voting method reached 66.416 percent. Following that, ensemble learning using the stacking method combined with the ANFIS kernel reached 60.6616 percent. Finally, the findings suggest that it may improve accuracy and reduce standard deviation depending on the Ensemble Learning approach used. It asserts that when the variance of the predictions was reduced, the classification model produced better results.","PeriodicalId":286966,"journal":{"name":"2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125245448","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}
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