Pub Date : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120573
J. Ocampo, Jonathan A. Dizon, Clarence Vinzcent I. Reyes, John Joseph C. Capitulo, Juncarl Kevin G. Tapang, Seigfred V. Prado
Fall events are common to elderly people due to their deteriorating muscle structures caused by old age. Their relatively weaker bodies make them prone to accidents such as falls even when performing daily tasks. These fall events may leave physical or psychological consequences among them. Commonly, these events are associated with one or more identifiable risk factors such as weakness, unsteady gait, confusion, environment, and certain medications. Previous researches have shown that these events can be prevented using fall detection mechanisms. In this study, we investigate whether the analysis of muscle fatigue degree may enhance the performance of existing fall detection systems that utilize both surface electromyography (SEMG) and accelerometer (ACC) sensors. SEMG and ACC signals were measured and recorded from 20 healthy study volunteers. A series of pre-defined activities that mimic fall events were performed by the study volunteers. These activities were conducted in a controlled environment. Acquired SEMG signals were pre-processed to eliminate unwanted signals and distortion. Discriminative features were then extracted from the clean signals, and these extracted features were combined with the accelerometer data for classification using an Artificial Neural Network (ANN) classifier. Results showed that the combination of SEMG and ACC data have relatively increased the accuracy of fall detection systems.
{"title":"Evaluation of muscle fatigue degree using surface electromyography and accelerometer signals in fall detection systems","authors":"J. Ocampo, Jonathan A. Dizon, Clarence Vinzcent I. Reyes, John Joseph C. Capitulo, Juncarl Kevin G. Tapang, Seigfred V. Prado","doi":"10.1109/ICSIPA.2017.8120573","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120573","url":null,"abstract":"Fall events are common to elderly people due to their deteriorating muscle structures caused by old age. Their relatively weaker bodies make them prone to accidents such as falls even when performing daily tasks. These fall events may leave physical or psychological consequences among them. Commonly, these events are associated with one or more identifiable risk factors such as weakness, unsteady gait, confusion, environment, and certain medications. Previous researches have shown that these events can be prevented using fall detection mechanisms. In this study, we investigate whether the analysis of muscle fatigue degree may enhance the performance of existing fall detection systems that utilize both surface electromyography (SEMG) and accelerometer (ACC) sensors. SEMG and ACC signals were measured and recorded from 20 healthy study volunteers. A series of pre-defined activities that mimic fall events were performed by the study volunteers. These activities were conducted in a controlled environment. Acquired SEMG signals were pre-processed to eliminate unwanted signals and distortion. Discriminative features were then extracted from the clean signals, and these extracted features were combined with the accelerometer data for classification using an Artificial Neural Network (ANN) classifier. Results showed that the combination of SEMG and ACC data have relatively increased the accuracy of fall detection systems.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126776868","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120624
ThasarathaRao Supramaniam, R. Ibrahim, S. Hassan, Kishore Bingi
The advancement of wireless technology is becoming more apparent in industrial sectors with the advent of standards such as WirelessHART. The benefits associated with WirelessHART includes elimination of costly and cumbersome cabling, reduced maintenance cost and reduced deployment, redeployment in the network. However, the current WirelessHART is lack of low cost adapter for monitoring and control application in process industries. In this work, a low cost WirelessHART adapter is developed using a mote and microcontroller (Arduino Mega 2560) for process monitoring application. Experimental results with temperature transmitter shows that the developed adapter is successfully interfaced and the temperature data is monitored continuously at a satisfactory delay.
随着诸如WirelessHART等标准的出现，无线技术的进步在工业领域变得越来越明显。与wireless shart相关的好处包括消除了昂贵和繁琐的布线，降低了维护成本，减少了在网络中的部署和重新部署。然而，目前的WirelessHART缺乏低成本的适配器用于过程工业的监测和控制应用。在这项工作中，使用mote和微控制器(Arduino Mega 2560)开发了一种低成本的无线shart适配器，用于过程监控应用。与温度变送器的实验结果表明，所开发的适配器接口成功，并能以满意的延迟连续监测温度数据。
{"title":"Development of WirelessHART adapter with industrial transmitter for process monitoring","authors":"ThasarathaRao Supramaniam, R. Ibrahim, S. Hassan, Kishore Bingi","doi":"10.1109/ICSIPA.2017.8120624","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120624","url":null,"abstract":"The advancement of wireless technology is becoming more apparent in industrial sectors with the advent of standards such as WirelessHART. The benefits associated with WirelessHART includes elimination of costly and cumbersome cabling, reduced maintenance cost and reduced deployment, redeployment in the network. However, the current WirelessHART is lack of low cost adapter for monitoring and control application in process industries. In this work, a low cost WirelessHART adapter is developed using a mote and microcontroller (Arduino Mega 2560) for process monitoring application. Experimental results with temperature transmitter shows that the developed adapter is successfully interfaced and the temperature data is monitored continuously at a satisfactory delay.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126225917","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120606
Mohammad Khateri, H. Ghassemian
Due to the importance of high-resolution multi-spectral (HRM) images in many remote sensing applications, pan-sharpening techniques have been proposed to increase the spatial resolution of a low-resolution multi-spectral (LRM) image using a high-resolution panchromatic (HRP) image. In this paper, we propose a self-learning approach to pan-sharpen the LRM images. Many structures in a natural image redundantly tend to repeat in the same scale as well as different scales. These similar structures in different levels can be used to reconstruct the HRM bands with more details; in this perspective, we can construct the HRM data from the available HRP and LRM data by using self-similarity in a multi-scale procedure. The proposed method has been applied on GeoEye-1 data and DEIMOS-2 data, and then fused images compared with some popular and state-of-the-art methods in terms of several assessment indexes. The experimental results demonstrate that the proposed method can retain spectral and spatial information of the source images efficiently.
{"title":"A self-learning approach for pan-sharpening of multispectral images","authors":"Mohammad Khateri, H. Ghassemian","doi":"10.1109/ICSIPA.2017.8120606","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120606","url":null,"abstract":"Due to the importance of high-resolution multi-spectral (HRM) images in many remote sensing applications, pan-sharpening techniques have been proposed to increase the spatial resolution of a low-resolution multi-spectral (LRM) image using a high-resolution panchromatic (HRP) image. In this paper, we propose a self-learning approach to pan-sharpen the LRM images. Many structures in a natural image redundantly tend to repeat in the same scale as well as different scales. These similar structures in different levels can be used to reconstruct the HRM bands with more details; in this perspective, we can construct the HRM data from the available HRP and LRM data by using self-similarity in a multi-scale procedure. The proposed method has been applied on GeoEye-1 data and DEIMOS-2 data, and then fused images compared with some popular and state-of-the-art methods in terms of several assessment indexes. The experimental results demonstrate that the proposed method can retain spectral and spatial information of the source images efficiently.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125886436","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120667
Shengda Luo, A. Leung, Yong Liang
In this paper, a novel approach to identifying superpixels in the cluttered environment is proposed. In our proposed method, the temporal cue and depth maps obtained from depth sensors are combined with the popular method SLIC for superpixels using a new formulation of distance-minimizing clustering. Under cluttered environment, this proposed method can, compared with color-based approaches, better identify the contour of objects. Experiments have been carried out using a public dataset to compare our approach to other methods. The experimental results demonstrate that our approach outperforms other approaches.
{"title":"A novel superpixel approach utilizing depth and temporal cues","authors":"Shengda Luo, A. Leung, Yong Liang","doi":"10.1109/ICSIPA.2017.8120667","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120667","url":null,"abstract":"In this paper, a novel approach to identifying superpixels in the cluttered environment is proposed. In our proposed method, the temporal cue and depth maps obtained from depth sensors are combined with the popular method SLIC for superpixels using a new formulation of distance-minimizing clustering. Under cluttered environment, this proposed method can, compared with color-based approaches, better identify the contour of objects. Experiments have been carried out using a public dataset to compare our approach to other methods. The experimental results demonstrate that our approach outperforms other approaches.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995067","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120651
S. Singh, G. Bhatnagar
In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize hash sequence which can be used for image authentication and database search. For this purpose, the image is first normalized followed by hash generation in the wavelet domain utilizing the properties of singular value decomposition (SVD). Experimental evaluations demonstrate that the proposed scheme is providing the better robustness and security.
{"title":"A robust image hashing based on discrete wavelet transform","authors":"S. Singh, G. Bhatnagar","doi":"10.1109/ICSIPA.2017.8120651","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120651","url":null,"abstract":"In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize hash sequence which can be used for image authentication and database search. For this purpose, the image is first normalized followed by hash generation in the wavelet domain utilizing the properties of singular value decomposition (SVD). Experimental evaluations demonstrate that the proposed scheme is providing the better robustness and security.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585138","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120653
H. Tan, K. Lim, H. Harno
Deep structured of Convolutional Neural Networks (CNN) has recently gained intense attention in development due to its good performance in object recognition. One of the crucial components in CNN is the learning mechanism of weight parameters through backpropagation. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) is proposed to train weight parameters in CNN. SDAGD adopts the concept of multistage control system and diagonal Hessian approximation for weight optimization. It can be defined into two-phase optimization. In phase 1, when an initial guessing point is far from the solution, SDAGD constructs local search regions to determine the step length of next iteration at the boundary of search region. Subsequently, when the solution is at the final search region, SDAGD will shift to phase 2 by approximating Newton method to obtain a fast weight convergence. The calculation of Hessian in diagonal approximation results in less computational cost as compared to full Hessian calculation. The experiment showed that SDAGD learning algorithm could achieve misclassification rate of 8.85% on MNIST dataset.
{"title":"Stochastic diagonal approximate greatest descent in convolutional neural networks","authors":"H. Tan, K. Lim, H. Harno","doi":"10.1109/ICSIPA.2017.8120653","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120653","url":null,"abstract":"Deep structured of Convolutional Neural Networks (CNN) has recently gained intense attention in development due to its good performance in object recognition. One of the crucial components in CNN is the learning mechanism of weight parameters through backpropagation. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) is proposed to train weight parameters in CNN. SDAGD adopts the concept of multistage control system and diagonal Hessian approximation for weight optimization. It can be defined into two-phase optimization. In phase 1, when an initial guessing point is far from the solution, SDAGD constructs local search regions to determine the step length of next iteration at the boundary of search region. Subsequently, when the solution is at the final search region, SDAGD will shift to phase 2 by approximating Newton method to obtain a fast weight convergence. The calculation of Hessian in diagonal approximation results in less computational cost as compared to full Hessian calculation. The experiment showed that SDAGD learning algorithm could achieve misclassification rate of 8.85% on MNIST dataset.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133951177","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120669
Silvia Joseph, Hamimah Ujir, I. Hipiny
Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.
{"title":"Unsupervised classification of Intrusive igneous rock thin section images using edge detection and colour analysis","authors":"Silvia Joseph, Hamimah Ujir, I. Hipiny","doi":"10.1109/ICSIPA.2017.8120669","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120669","url":null,"abstract":"Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121277852","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120619
Faruq Muhammad Foong, C. Thein, B. L. Ooi, A. Aziz
This paper proposes a low-cost vibration analyser for a vibration-based shaker. The system comprises a refurbished analogue shaker, laser displacement sensors and data acquisition (DAQ) device. The laser displacement sensor captures the vibrational motion generated by the shaker. The data obtained from the laser displacement sensor is then amplified and transferred to a computer using a DAQ device. The system demonstrated close proximity to the analytical and simulation results in terms of determining the natural frequency of a cantilever beam, hence verifying the capability of the system for vibration analysis. The limitations of the system come from the analogue shaker and the sensitivity of the laser displacement sensors. Due to the attribute of an analogue machine, the shaker can only be operated manually. A sweep run is preferred to accommodate the low precision of the shaker. A frequency sweep speed between the range of 0.03 to 0.07 Hz per second should be used to ensure that the true amplitude of the test specimen can be obtained. A sampling rate of 20 kHz for the DAQ device is sufficient for the proposed system, while maintaining a proportional data size.
{"title":"A low-cost vibration analyser for analogue electromagnetic shaker","authors":"Faruq Muhammad Foong, C. Thein, B. L. Ooi, A. Aziz","doi":"10.1109/ICSIPA.2017.8120619","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120619","url":null,"abstract":"This paper proposes a low-cost vibration analyser for a vibration-based shaker. The system comprises a refurbished analogue shaker, laser displacement sensors and data acquisition (DAQ) device. The laser displacement sensor captures the vibrational motion generated by the shaker. The data obtained from the laser displacement sensor is then amplified and transferred to a computer using a DAQ device. The system demonstrated close proximity to the analytical and simulation results in terms of determining the natural frequency of a cantilever beam, hence verifying the capability of the system for vibration analysis. The limitations of the system come from the analogue shaker and the sensitivity of the laser displacement sensors. Due to the attribute of an analogue machine, the shaker can only be operated manually. A sweep run is preferred to accommodate the low precision of the shaker. A frequency sweep speed between the range of 0.03 to 0.07 Hz per second should be used to ensure that the true amplitude of the test specimen can be obtained. A sampling rate of 20 kHz for the DAQ device is sufficient for the proposed system, while maintaining a proportional data size.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121296323","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120575
V. Sowmya, A. Ajay, D. Govind, K. Soman
In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM.
{"title":"Improved color scene classification system using deep belief networks and support vector machines","authors":"V. Sowmya, A. Ajay, D. Govind, K. Soman","doi":"10.1109/ICSIPA.2017.8120575","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120575","url":null,"abstract":"In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121067985","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 : 2017-09-01DOI: 10.1109/ICSIPA.2017.8120579
Zee Ang Sim, R. Reine, Z. Zang, Lenin Gopal
The challenges faced in the design of multiuser multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems are the high peak-to-average power ratio (PAPR) in the transmitted signal and interference occured among the different users. Shaping the subcarriers in a proper way can reduce the PAPR of the OFDM signal effectively and minimize the interference among users. This paper proposes to use computationally efficient optimization method to design a set of waveforms to shape the subcarriers for PAPR reduction and BER improvement in the MU-MIMO-OFDM systems. Numerical results illustrate that the designed set of pulse shaping waveforms is efficient in reducing the PAPR of the transmitted signal while improving the BER in MU-MIMO-OFDM systems.
{"title":"PAPR and BER reduction in MU-MIMO-OFDM systems via a set of waveforms","authors":"Zee Ang Sim, R. Reine, Z. Zang, Lenin Gopal","doi":"10.1109/ICSIPA.2017.8120579","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120579","url":null,"abstract":"The challenges faced in the design of multiuser multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems are the high peak-to-average power ratio (PAPR) in the transmitted signal and interference occured among the different users. Shaping the subcarriers in a proper way can reduce the PAPR of the OFDM signal effectively and minimize the interference among users. This paper proposes to use computationally efficient optimization method to design a set of waveforms to shape the subcarriers for PAPR reduction and BER improvement in the MU-MIMO-OFDM systems. Numerical results illustrate that the designed set of pulse shaping waveforms is efficient in reducing the PAPR of the transmitted signal while improving the BER in MU-MIMO-OFDM systems.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428677","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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