Pub Date : 2018-02-01DOI: 10.1109/NCC.2018.8599918
G. Suresh, Abhishek Chakraborty, B. S. Manoj
Complex networks are abstract graphs where the nodes are real-world entities and their relationships can be imitated as links. The relationships among various real-world entities are neither entirely random nor fully regular, thus, the structures of evolving complex networks are non-trivial in nature. To study the characteristics of such evolving complex networks, efficient network models, that can emulate the realworld networks, are needed. In this paper, a novel network model, deterministic evolution through leaf node attachment (DELNA), that can efficiently emulate many real-world networks, such as technological networks, social networks, and other manmade networks, is proposed. We also compare our DELNA-based network model with a few existing network evolution models, namely Barabási-Ravasz-Vicsck deterministic network model and Barabási-Albert network evolution model. DELNA can find applications in the Internet of things and the satellite communications, where the network resilience is a very crucial parameter.
{"title":"Deterministic Evolution Through Indexed Leaf Node Based Attachment in Complex Networks","authors":"G. Suresh, Abhishek Chakraborty, B. S. Manoj","doi":"10.1109/NCC.2018.8599918","DOIUrl":"https://doi.org/10.1109/NCC.2018.8599918","url":null,"abstract":"Complex networks are abstract graphs where the nodes are real-world entities and their relationships can be imitated as links. The relationships among various real-world entities are neither entirely random nor fully regular, thus, the structures of evolving complex networks are non-trivial in nature. To study the characteristics of such evolving complex networks, efficient network models, that can emulate the realworld networks, are needed. In this paper, a novel network model, deterministic evolution through leaf node attachment (DELNA), that can efficiently emulate many real-world networks, such as technological networks, social networks, and other manmade networks, is proposed. We also compare our DELNA-based network model with a few existing network evolution models, namely Barabási-Ravasz-Vicsck deterministic network model and Barabási-Albert network evolution model. DELNA can find applications in the Internet of things and the satellite communications, where the network resilience is a very crucial parameter.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121084779","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 : 2018-02-01DOI: 10.1109/NCC.2018.8599934
Kamini Sabu, Kanhaiya Kumar, P. Rao
Reading skill is a critical component of basic literacy. We aim to develop an automated system to assess oral reading skills of primary school children (learning English as a second language) that could eventually be valuable in the scenario of teacher shortage typical of rural areas in the country. This work focuses on the rating of prosody, an important aspect of fluency in speech delivery. In particular, a system for the detection of word prominence based on prosodic features is presented and tested on real-world data marked by background noise typical of the school setting. To counteract the observed drop in prominence classification accuracy, two distinct approaches to noisy speech enhancement are evaluated for various types of background noise. A recently proposed Generative Adversarial Network(GAN) based method is found to be effective in achieving noise suppression with low levels of speech distortion that minimally impact prosodic feature extraction. The implementation and training of the GAN system is discussed and insights are provided on its performance with reference to that of classical spectral subtraction based enhancement.
{"title":"Improving the Noise Robustness of Prominence Detection for Children's Oral Reading Assessment","authors":"Kamini Sabu, Kanhaiya Kumar, P. Rao","doi":"10.1109/NCC.2018.8599934","DOIUrl":"https://doi.org/10.1109/NCC.2018.8599934","url":null,"abstract":"Reading skill is a critical component of basic literacy. We aim to develop an automated system to assess oral reading skills of primary school children (learning English as a second language) that could eventually be valuable in the scenario of teacher shortage typical of rural areas in the country. This work focuses on the rating of prosody, an important aspect of fluency in speech delivery. In particular, a system for the detection of word prominence based on prosodic features is presented and tested on real-world data marked by background noise typical of the school setting. To counteract the observed drop in prominence classification accuracy, two distinct approaches to noisy speech enhancement are evaluated for various types of background noise. A recently proposed Generative Adversarial Network(GAN) based method is found to be effective in achieving noise suppression with low levels of speech distortion that minimally impact prosodic feature extraction. The implementation and training of the GAN system is discussed and insights are provided on its performance with reference to that of classical spectral subtraction based enhancement.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133842829","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 : 2018-02-01DOI: 10.1109/NCC.2018.8600070
T. Uday, Abhinav Kumar, L. Natarajan
In visible light communications (VLC), direct current (DC) level balancing is important to maintain constant illumination while the LEDs are being used for communication. In this paper, given codeword length of $n$ bits, we provide a relationship between maximum possible input bits, $k$, and $n$ for a perfectly DC balanced $kb/nb$ code. We propose an algorithm to generate the code book of these perfectly DC balanced codes that avoid flickering and maintain consistency in the brightness. The performance of the proposed codes is compared with several existing codes in terms of code rate, Hamming distance, frame error rate (FER), and bit error rate (BER). The numerical results show that the proposed codes provide perfect DC balance and perform better in terms of minimum Hamming distance, FER, and BER without significant loss in code rate. Further, we derive a lower bound on average Hamming distance for the proposed perfectly DC balanced codes for VLC.
{"title":"Generation of Perfectly DC Balanced Codes for Visible Light Communications","authors":"T. Uday, Abhinav Kumar, L. Natarajan","doi":"10.1109/NCC.2018.8600070","DOIUrl":"https://doi.org/10.1109/NCC.2018.8600070","url":null,"abstract":"In visible light communications (VLC), direct current (DC) level balancing is important to maintain constant illumination while the LEDs are being used for communication. In this paper, given codeword length of $n$ bits, we provide a relationship between maximum possible input bits, $k$, and $n$ for a perfectly DC balanced $kb/nb$ code. We propose an algorithm to generate the code book of these perfectly DC balanced codes that avoid flickering and maintain consistency in the brightness. The performance of the proposed codes is compared with several existing codes in terms of code rate, Hamming distance, frame error rate (FER), and bit error rate (BER). The numerical results show that the proposed codes provide perfect DC balance and perform better in terms of minimum Hamming distance, FER, and BER without significant loss in code rate. Further, we derive a lower bound on average Hamming distance for the proposed perfectly DC balanced codes for VLC.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125615842","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 : 2018-02-01DOI: 10.1109/NCC.2018.8600083
Tejaswini Dudyala, Srivally Munnangi, S. Mani
Multiple Signal Classification (MUSIC), Steered Response Power-PHAse Transform (SRP-PHAT) and Generalized Cross Correlation (GCC) are the well known techniques for Direction of Arrival (DoA) estimation, using microphone array. However, in real time scenarios, these techniques encounter limitations such as computational complexity and thresholding difficulties. In this paper, a novel and robust method is introduced in which DoA is estimated using the concept of subarray decomposition to provide better performance with effective thresholding and minimal computational complexity.
{"title":"Grouping Subarray for Robust Estimation of Direction of Arrival","authors":"Tejaswini Dudyala, Srivally Munnangi, S. Mani","doi":"10.1109/NCC.2018.8600083","DOIUrl":"https://doi.org/10.1109/NCC.2018.8600083","url":null,"abstract":"Multiple Signal Classification (MUSIC), Steered Response Power-PHAse Transform (SRP-PHAT) and Generalized Cross Correlation (GCC) are the well known techniques for Direction of Arrival (DoA) estimation, using microphone array. However, in real time scenarios, these techniques encounter limitations such as computational complexity and thresholding difficulties. In this paper, a novel and robust method is introduced in which DoA is estimated using the concept of subarray decomposition to provide better performance with effective thresholding and minimal computational complexity.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562635","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 : 2018-02-01DOI: 10.1109/NCC.2018.8600185
R. Renu, V. Sowmya, K. Soman
Hyperspectral images are large cubes of data which are commonly processed band-wise as two-dimensional image patches. This 2D processing might lead to loose the spectral efficiency contained in the image. Introducing Hyperspectral image as third-order tensors helps to preserve the spectral and spatial efficiency of the image. Multilinear Singular Value Decomposition (MLSVD) is an extension of Singular Value Decomposition (SVD) to 3D which can be used for compressing the image spatially and spectrally. The efficiency of compression is verified by reconstructing the image using Low Multilinear Rank Approximation (LMLRA). The proposed method has been validated with Signal to Noise Ratio (SNR), pixel reflectance spectrum and pixel-wise classification of the reconstructed image.
{"title":"Spatio-Spectral Compression and Analysis of Hyperspectral Images using Tensor Decomposition","authors":"R. Renu, V. Sowmya, K. Soman","doi":"10.1109/NCC.2018.8600185","DOIUrl":"https://doi.org/10.1109/NCC.2018.8600185","url":null,"abstract":"Hyperspectral images are large cubes of data which are commonly processed band-wise as two-dimensional image patches. This 2D processing might lead to loose the spectral efficiency contained in the image. Introducing Hyperspectral image as third-order tensors helps to preserve the spectral and spatial efficiency of the image. Multilinear Singular Value Decomposition (MLSVD) is an extension of Singular Value Decomposition (SVD) to 3D which can be used for compressing the image spatially and spectrally. The efficiency of compression is verified by reconstructing the image using Low Multilinear Rank Approximation (LMLRA). The proposed method has been validated with Signal to Noise Ratio (SNR), pixel reflectance spectrum and pixel-wise classification of the reconstructed image.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130861136","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 : 2018-02-01DOI: 10.1109/NCC.2018.8600011
Dheeraj Kumar Chittam, R. Bansal, R. Srivastava
This paper focuses on universal compression of a piecewise stationary source using sequential change detection algorithms. The change detection algorithms that we have considered assume minimal knowledge of the source and make use of universal estimators of entropy. Here, data in each segment is characterized either by an I.I.D. random process or a first order Markov process. Simulation study of a modified sequential change detection test proposed by Jacob and Bansal [1] is carried out. Next, an algorithm to effectively compress a piece-wise stationary sequence using such change detection algorithms is proposed. Overall compression efficiency achieved with Page's Cumulative Sum (CUSUM) test and the modified change detection test proposed in [1] (JB-Page test) as part of the change detection schemes, are compared. Further, when JB-Page test is used for change detection, four different compression algorithms, namely, Lempel Ziv Welch (LZW), Lempel Ziv (LZ78), Burrows Wheeler Transform (BWT) and Context Tree Weighting (CTW) algorithms are compared based on their impact on overall compression.
{"title":"Universal Compression of a Piecewise Stationary Source Through Sequential Change Detection","authors":"Dheeraj Kumar Chittam, R. Bansal, R. Srivastava","doi":"10.1109/NCC.2018.8600011","DOIUrl":"https://doi.org/10.1109/NCC.2018.8600011","url":null,"abstract":"This paper focuses on universal compression of a piecewise stationary source using sequential change detection algorithms. The change detection algorithms that we have considered assume minimal knowledge of the source and make use of universal estimators of entropy. Here, data in each segment is characterized either by an I.I.D. random process or a first order Markov process. Simulation study of a modified sequential change detection test proposed by Jacob and Bansal [1] is carried out. Next, an algorithm to effectively compress a piece-wise stationary sequence using such change detection algorithms is proposed. Overall compression efficiency achieved with Page's Cumulative Sum (CUSUM) test and the modified change detection test proposed in [1] (JB-Page test) as part of the change detection schemes, are compared. Further, when JB-Page test is used for change detection, four different compression algorithms, namely, Lempel Ziv Welch (LZW), Lempel Ziv (LZ78), Burrows Wheeler Transform (BWT) and Context Tree Weighting (CTW) algorithms are compared based on their impact on overall compression.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114680738","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 : 2018-02-01DOI: 10.1109/NCC.2018.8599983
Vinay kumar Trivedi, Preetam Kumar
Multiple Input Multiple Output (MIMO) provides robustness against multipath fading using spatial diversity and increases data rate by spatial multiplexing. These gains are achieved by using multiple antennas. This paper presents multi user (MU) MIMO downlink system under imperfect channel state information (CSI) and heterogeneous user signal-to-noise ratio (SNR) prole. MU MIMO essentially provides MU diversity gain. System capacity is increased by choosing the users experiencing better channel parameters. We have used normalized SNR based scheduler to select a user for data transmission. The outcomes of these techniques are higher data rate and transmitting long range without need of much power or bandwidth. In this paper, a comparative BER performance evaluation of MU MIMO-OFDM and MU MIMO-SCFDMA has been presented and along. Moreover the effects of various factors such as imperfection of channel state information (CSI), heterogeneity of network are investigated for these schemes.
{"title":"BER Performance of Multi User Scheduling for MIMO-OFDM and MIMO-SCFDMA Broadcast Network with Imperfect CSI","authors":"Vinay kumar Trivedi, Preetam Kumar","doi":"10.1109/NCC.2018.8599983","DOIUrl":"https://doi.org/10.1109/NCC.2018.8599983","url":null,"abstract":"Multiple Input Multiple Output (MIMO) provides robustness against multipath fading using spatial diversity and increases data rate by spatial multiplexing. These gains are achieved by using multiple antennas. This paper presents multi user (MU) MIMO downlink system under imperfect channel state information (CSI) and heterogeneous user signal-to-noise ratio (SNR) prole. MU MIMO essentially provides MU diversity gain. System capacity is increased by choosing the users experiencing better channel parameters. We have used normalized SNR based scheduler to select a user for data transmission. The outcomes of these techniques are higher data rate and transmitting long range without need of much power or bandwidth. In this paper, a comparative BER performance evaluation of MU MIMO-OFDM and MU MIMO-SCFDMA has been presented and along. Moreover the effects of various factors such as imperfection of channel state information (CSI), heterogeneity of network are investigated for these schemes.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123061533","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 : 2018-02-01DOI: 10.1109/NCC.2018.8599894
V. Sukumaran, C. Singh
Communication devices such as cellular phones are now capable of using different communication technologies, such as 3G/4G and WiFi. The high data rate requirements of current user applications have lead to the important engineering problem of optimally associating a communication device with different networks using different communication technologies so as to maximize data rates. In this paper, we study the problem of optimally associating a group of users to a single base station and a single WiFi access point so that the sum throughput of all the users is maximized. Compared to prior work, we find that under realistic models for throughputs which are achievable on WiFi, the above association problem is a fractional programming problem, namely a “maximising sum of ratios” problem. Although the general sum of ratios problem is hard to solve, for the above association problem, we suggest heuristic algorithms that are shown to have a sum throughput close to the maximum. We also suggest a novel upper bound to the maximum sum throughput which has applications in performance evaluation.
{"title":"Optimal association of wireless devices to cellular and Wi-Fi base stations","authors":"V. Sukumaran, C. Singh","doi":"10.1109/NCC.2018.8599894","DOIUrl":"https://doi.org/10.1109/NCC.2018.8599894","url":null,"abstract":"Communication devices such as cellular phones are now capable of using different communication technologies, such as 3G/4G and WiFi. The high data rate requirements of current user applications have lead to the important engineering problem of optimally associating a communication device with different networks using different communication technologies so as to maximize data rates. In this paper, we study the problem of optimally associating a group of users to a single base station and a single WiFi access point so that the sum throughput of all the users is maximized. Compared to prior work, we find that under realistic models for throughputs which are achievable on WiFi, the above association problem is a fractional programming problem, namely a “maximising sum of ratios” problem. Although the general sum of ratios problem is hard to solve, for the above association problem, we suggest heuristic algorithms that are shown to have a sum throughput close to the maximum. We also suggest a novel upper bound to the maximum sum throughput which has applications in performance evaluation.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123910716","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 : 2018-02-01DOI: 10.1109/NCC.2018.8600267
Anil Kumar Chilli, K. R. Prasanna Kumar, H. Murthy, C. Sekhar
The performance of automatic speaker identification (ASI) systems on Voice over Internet Protocol (VoIP) speech varies with the type of codec used in the VoIP communication. The type of codec used depends on the service provider of the user. Thus there is a need for the codec-independent ASI systems to identify the speaker. Three modeling approaches based on UBM-GMM framework and i-vector framework are proposed to identify the speaker independent of codec used. These frameworks are also evaluated for the mismatch conditions with respect to the codec used in training and testing. The proposed approaches are evaluated on VoIP speech from four codecs with different bit rates along with uncoded speech.
自动说话人识别(ASI)系统在VoIP (Voice over Internet Protocol)语音上的性能随VoIP通信中使用的编解码器类型的不同而不同。所使用的编解码器类型取决于用户的服务提供商。因此，需要独立于编解码器的ASI系统来识别说话人。提出了基于UBM-GMM框架和i-vector框架的三种独立于编解码器的说话人识别方法。这些框架还评估了与训练和测试中使用的编解码器相关的不匹配条件。在四种不同码率的VoIP语音以及未编码语音上对所提出的方法进行了评估。
{"title":"Approaches to Codec Independent Speaker Identification in Voip Speech","authors":"Anil Kumar Chilli, K. R. Prasanna Kumar, H. Murthy, C. Sekhar","doi":"10.1109/NCC.2018.8600267","DOIUrl":"https://doi.org/10.1109/NCC.2018.8600267","url":null,"abstract":"The performance of automatic speaker identification (ASI) systems on Voice over Internet Protocol (VoIP) speech varies with the type of codec used in the VoIP communication. The type of codec used depends on the service provider of the user. Thus there is a need for the codec-independent ASI systems to identify the speaker. Three modeling approaches based on UBM-GMM framework and i-vector framework are proposed to identify the speaker independent of codec used. These frameworks are also evaluated for the mismatch conditions with respect to the codec used in training and testing. The proposed approaches are evaluated on VoIP speech from four codecs with different bit rates along with uncoded speech.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"28 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107378","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 : 2018-02-01DOI: 10.1109/NCC.2018.8599898
B. Dileep, Tapan Das, P. Dutta
Diffuse optical tomography (DOT) is a low cost imaging modality that reconstructs the optical coefficients of a highly turbid medium. However, the inverse problem is ill-posed, nonlinear, and unstable due to diffusive nature of optical photons through the biological tissue. The conventional DOT imaging methods require the forward problem to be solved repeatedly at each iteration which makes it computationally expensive. Recently, the theory of compressive sensing (CS) has been used in DOT and provided significant reconstruction of sparse objects in many DOT imaging problems. The main objective of this paper is to solve the DOT inverse problem using MMV (multiple measurement vectors) based CS framework and the sparse recovery algorithm like CS-MUSIC (multiple signal classification) is studied. The experimental validation of the CS-MUSIC has been done on a paraffin wax rectangular sample through a DOT experimental set up. We also studied the conventional DOT imaging method like least square method in this paper. The performance metric mean square error (MSE) is used to evaluate the performance of the reconstruction in DOT imaging. Simulation results showed that the CS-MUSIC algorithm outperforms the conventional DOT imaging method in DOT imaging. The advantage of this study is that the forward problem need not be solved repeatedly which are inherent in conventional DOT.
{"title":"Subspace Based CS-Music for Diffuse Optical Tomography","authors":"B. Dileep, Tapan Das, P. Dutta","doi":"10.1109/NCC.2018.8599898","DOIUrl":"https://doi.org/10.1109/NCC.2018.8599898","url":null,"abstract":"Diffuse optical tomography (DOT) is a low cost imaging modality that reconstructs the optical coefficients of a highly turbid medium. However, the inverse problem is ill-posed, nonlinear, and unstable due to diffusive nature of optical photons through the biological tissue. The conventional DOT imaging methods require the forward problem to be solved repeatedly at each iteration which makes it computationally expensive. Recently, the theory of compressive sensing (CS) has been used in DOT and provided significant reconstruction of sparse objects in many DOT imaging problems. The main objective of this paper is to solve the DOT inverse problem using MMV (multiple measurement vectors) based CS framework and the sparse recovery algorithm like CS-MUSIC (multiple signal classification) is studied. The experimental validation of the CS-MUSIC has been done on a paraffin wax rectangular sample through a DOT experimental set up. We also studied the conventional DOT imaging method like least square method in this paper. The performance metric mean square error (MSE) is used to evaluate the performance of the reconstruction in DOT imaging. Simulation results showed that the CS-MUSIC algorithm outperforms the conventional DOT imaging method in DOT imaging. The advantage of this study is that the forward problem need not be solved repeatedly which are inherent in conventional DOT.","PeriodicalId":121544,"journal":{"name":"2018 Twenty Fourth National Conference on Communications (NCC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121195252","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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