Pub Date : 2020-02-01DOI: 10.1109/NCC48643.2020.9056092
Brajesh K. Shukla, Hiteshi Jain, V. Vijay, S. Yadav, D. Hewson
Automated clinical tests that assess quality of geriatric screening tests such as the Five-Times-Sit- To-Stand (5STS) and the Timed-Up-and-Go (TUG) are being designed to assess the decline in functional ability of elderly. The existing techniques to assess the quality of these physical activities include sensor-based techniques including body mounted sensors, force sensors and, vision and imaging sensors. These sensors have their own advantages and disadvantages towards the task of clinical assessment. In this work, we introduce a fusion- based technique to combine multiple sensors leveraging advantages of individual sensors, in such a way that the resulting assessment is more accurate. We evaluate our technique for 5STS test using a fusion of a chair and RGB sensors. In a test of 15 older people, there was no significant difference in performance between the two sensors, obtaining 76% and 73% for the RGB and chair, respectively. However, a significant improvement was obtained for the fusion technique, with 90% accuracy for all the phases of the STS test. The proposed fusion technique was observed to be better than the individual sensor assessment.
{"title":"A Fusion-Based Approach to Identify the Phases of the Sit-to-Stand Test in Older People","authors":"Brajesh K. Shukla, Hiteshi Jain, V. Vijay, S. Yadav, D. Hewson","doi":"10.1109/NCC48643.2020.9056092","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056092","url":null,"abstract":"Automated clinical tests that assess quality of geriatric screening tests such as the Five-Times-Sit- To-Stand (5STS) and the Timed-Up-and-Go (TUG) are being designed to assess the decline in functional ability of elderly. The existing techniques to assess the quality of these physical activities include sensor-based techniques including body mounted sensors, force sensors and, vision and imaging sensors. These sensors have their own advantages and disadvantages towards the task of clinical assessment. In this work, we introduce a fusion- based technique to combine multiple sensors leveraging advantages of individual sensors, in such a way that the resulting assessment is more accurate. We evaluate our technique for 5STS test using a fusion of a chair and RGB sensors. In a test of 15 older people, there was no significant difference in performance between the two sensors, obtaining 76% and 73% for the RGB and chair, respectively. However, a significant improvement was obtained for the fusion technique, with 90% accuracy for all the phases of the STS test. The proposed fusion technique was observed to be better than the individual sensor assessment.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114571557","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056008
S. S. Behera, Bappaditya Mandal, N. Puhan
Recognition of individuals using periocular information has received significant importance due to its advantages over other biometric traits such as face and iris in challenging scenarios where it is difficult to acquire either full facial region or iris images. Recent surveillance applications give rise to a challenging research problem where individuals are recognized in cross-spectral environments in which a probe infra-red (IR) image is matched with a gallery of visible (VIS) images and vice versa. Cross-spectral recognition has been studied mostly for face and iris traits over the past few years; however, the performance of periocular biometric in the cross-spectral domain still needs to be improved. In this paper, we propose a twin deep convolutional neural network (TCNN) with shared parameters to match VIS periocular images with those of near IR (NIR) ones. The proposed TCNN finds the similarity between the VIS and NIR image pairs applied at its input rather than classifying them into a certain class. The learning mechanism involved in this network is such that the distance between the images corresponding to the genuine pairs is reduced and that of the imposter pairs is maximized. Based on the experimental results and analysis on three publicly available cross-spectral periocular databases, the TCNN achieves the state-of-the-art recognition results.
{"title":"Twin Deep Convolutional Neural Network-based Cross-spectral Periocular Recognition","authors":"S. S. Behera, Bappaditya Mandal, N. Puhan","doi":"10.1109/NCC48643.2020.9056008","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056008","url":null,"abstract":"Recognition of individuals using periocular information has received significant importance due to its advantages over other biometric traits such as face and iris in challenging scenarios where it is difficult to acquire either full facial region or iris images. Recent surveillance applications give rise to a challenging research problem where individuals are recognized in cross-spectral environments in which a probe infra-red (IR) image is matched with a gallery of visible (VIS) images and vice versa. Cross-spectral recognition has been studied mostly for face and iris traits over the past few years; however, the performance of periocular biometric in the cross-spectral domain still needs to be improved. In this paper, we propose a twin deep convolutional neural network (TCNN) with shared parameters to match VIS periocular images with those of near IR (NIR) ones. The proposed TCNN finds the similarity between the VIS and NIR image pairs applied at its input rather than classifying them into a certain class. The learning mechanism involved in this network is such that the distance between the images corresponding to the genuine pairs is reduced and that of the imposter pairs is maximized. Based on the experimental results and analysis on three publicly available cross-spectral periocular databases, the TCNN achieves the state-of-the-art recognition results.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117066692","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056095
Sudestna Nahak, G. Saha
In healthcare, Electrocardiogram (ECG) signal is considered important to study life-threatening heart diseases that include arrhythmia (ARR), congestive heart failure (CHF). Mostly, atrial arrhythmia leads to CHF. Previous studies on ARR and CHF are focused on the binary classification of each category against normal sinus rhythm (NSR). So, there is a requirement to study the above disease cases together to detect the severity of the situation and take remedial action accordingly. The goal of this study is to analyse and classify these three different classes of ECG (namely ARR, CHF, and NSR) in an efficient way. We used 30 ECG recordings for each of the classes from the publicly available Physionet database. Since the temporal and spectral features by themselves may be insufficient to distinguish the classes, we sought to combine information across both. Accordingly, we considered feature representations from heart rate variability (HRV) of the ECG signal and wavelet-based features together with auto-regressive coefficients. To leverage complementary information across feature types, we employed feature-level fusion. We examined the performance of individual and fused feature types with multiple classifiers. The highest accuracy of 93.33% for three-class classification was obtained after feature fusion using Support Vector Machine (SVM). Although the performance of HRV features is relatively poor compared to wavelet-based features, their fusion improved the classification accuracy.
{"title":"A Fusion Based Classification of Normal, Arrhythmia and Congestive Heart Failure in ECG","authors":"Sudestna Nahak, G. Saha","doi":"10.1109/NCC48643.2020.9056095","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056095","url":null,"abstract":"In healthcare, Electrocardiogram (ECG) signal is considered important to study life-threatening heart diseases that include arrhythmia (ARR), congestive heart failure (CHF). Mostly, atrial arrhythmia leads to CHF. Previous studies on ARR and CHF are focused on the binary classification of each category against normal sinus rhythm (NSR). So, there is a requirement to study the above disease cases together to detect the severity of the situation and take remedial action accordingly. The goal of this study is to analyse and classify these three different classes of ECG (namely ARR, CHF, and NSR) in an efficient way. We used 30 ECG recordings for each of the classes from the publicly available Physionet database. Since the temporal and spectral features by themselves may be insufficient to distinguish the classes, we sought to combine information across both. Accordingly, we considered feature representations from heart rate variability (HRV) of the ECG signal and wavelet-based features together with auto-regressive coefficients. To leverage complementary information across feature types, we employed feature-level fusion. We examined the performance of individual and fused feature types with multiple classifiers. The highest accuracy of 93.33% for three-class classification was obtained after feature fusion using Support Vector Machine (SVM). Although the performance of HRV features is relatively poor compared to wavelet-based features, their fusion improved the classification accuracy.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117141053","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056004
John Philip Bhimavarapu, S. Kalyan, V. K. Mittal
Emotion classification from emotional speech continues to be a challenging research domain. Few research studies have attempted to discriminate amongst a set of emotions, and categorize for valence, activation and dominance. Discriminating between high-arousal and low-arousal emotions is itself challenging, but discriminating emotions within each subcategory is further challenging problem. In this study, a new approach is proposed to discriminate between high and low arousal emotions, and also amongst emotions within each subcategory. Mahalanobis distances amongst acoustic feature vectors of emotional speech w.r.t. normal speech are examined. The approach, involving speech production features, has been validated on three databases: German (Berlin EMO-DB), English (RAVDESS) and Telugu (IITKGP-SESC). A common set of five emotions Angry, Happy, Fear, Disgust and Sad are examined with reference to normal speech. The vocal-tract filter features Mel-frequency cepstral coefficients (MFCCs), and combined source-filter features signal energy, zero-crossing rate and duration are used. A 2D projection of Mahalanobis distance for one emotion, w.r.t. normal, onto another emotion is observed to discriminate amongst emotions within each high/low-arousal sub-category. The Angry and Happy emotions are discriminated in high-arousal emotions sub-category, whereas Fear, Disgust and Sad are discriminated in low-arousal emotions sub-category. This study should be helpful in further classifying emotions within each subcategory of high/low arousal emotions in emotional speech.
{"title":"Discriminating High Arousal and Low Arousal Emotional Speech Using Mahalanobis Distance Among Acoustic Features","authors":"John Philip Bhimavarapu, S. Kalyan, V. K. Mittal","doi":"10.1109/NCC48643.2020.9056004","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056004","url":null,"abstract":"Emotion classification from emotional speech continues to be a challenging research domain. Few research studies have attempted to discriminate amongst a set of emotions, and categorize for valence, activation and dominance. Discriminating between high-arousal and low-arousal emotions is itself challenging, but discriminating emotions within each subcategory is further challenging problem. In this study, a new approach is proposed to discriminate between high and low arousal emotions, and also amongst emotions within each subcategory. Mahalanobis distances amongst acoustic feature vectors of emotional speech w.r.t. normal speech are examined. The approach, involving speech production features, has been validated on three databases: German (Berlin EMO-DB), English (RAVDESS) and Telugu (IITKGP-SESC). A common set of five emotions Angry, Happy, Fear, Disgust and Sad are examined with reference to normal speech. The vocal-tract filter features Mel-frequency cepstral coefficients (MFCCs), and combined source-filter features signal energy, zero-crossing rate and duration are used. A 2D projection of Mahalanobis distance for one emotion, w.r.t. normal, onto another emotion is observed to discriminate amongst emotions within each high/low-arousal sub-category. The Angry and Happy emotions are discriminated in high-arousal emotions sub-category, whereas Fear, Disgust and Sad are discriminated in low-arousal emotions sub-category. This study should be helpful in further classifying emotions within each subcategory of high/low arousal emotions in emotional speech.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128468617","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056041
P. Maheswaran, Mandha Damodaran Selvarai
Space shift keying (SSK) is a modulation technique that conveys information using the indices of the activated antenna. Media Based Modulation (MBM) proposed recently uses the ON/OFF status of radio frequency (RF) mirrors to create distinct channel fade realizations with a single transmit antenna. Multi RF chain Time successive SSK-$M$-ary modulation (MRF-TSSM) is a new second-order transmit diversity scheme. The number of antennas needed at the MRF-TSSM transmitter increases exponentially with spectral efficiency as it uses SSK. To overcome this, two system models are proposed in this work. Generalized TSSM (GTSSM) uses the activation of antenna combinations to reduce the antenna count. The condition on the combination of antennas to achieve second-order transmit diversity, the bit error rate (BER) performance and its asymptotic form are derived for GTSSM. MBM based MRF-TSSM (MBM-TSSM) exploits MBM to realize the SSK phase of MRF-TSSM with one antenna per modulator. Further for MBM-TSSM, a mirror activation pattern (MAP) selection criterion is shown and its improved diversity order is analyzed. Simulation results are provided to validate all the analysis. From the results, it is found that GTSSM with a lesser number of active antennas performs better. Moreover, MBM-TSSM is found to provide the same performance as MRF-TSSM. Based on the number of MAPs used for selection, the diversity order of MBM-TSSM is also found to increase.
{"title":"On Multi RF chain Time Successive SSK-M-ary Modulation Transmitter","authors":"P. Maheswaran, Mandha Damodaran Selvarai","doi":"10.1109/NCC48643.2020.9056041","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056041","url":null,"abstract":"Space shift keying (SSK) is a modulation technique that conveys information using the indices of the activated antenna. Media Based Modulation (MBM) proposed recently uses the ON/OFF status of radio frequency (RF) mirrors to create distinct channel fade realizations with a single transmit antenna. Multi RF chain Time successive SSK-$M$-ary modulation (MRF-TSSM) is a new second-order transmit diversity scheme. The number of antennas needed at the MRF-TSSM transmitter increases exponentially with spectral efficiency as it uses SSK. To overcome this, two system models are proposed in this work. Generalized TSSM (GTSSM) uses the activation of antenna combinations to reduce the antenna count. The condition on the combination of antennas to achieve second-order transmit diversity, the bit error rate (BER) performance and its asymptotic form are derived for GTSSM. MBM based MRF-TSSM (MBM-TSSM) exploits MBM to realize the SSK phase of MRF-TSSM with one antenna per modulator. Further for MBM-TSSM, a mirror activation pattern (MAP) selection criterion is shown and its improved diversity order is analyzed. Simulation results are provided to validate all the analysis. From the results, it is found that GTSSM with a lesser number of active antennas performs better. Moreover, MBM-TSSM is found to provide the same performance as MRF-TSSM. Based on the number of MAPs used for selection, the diversity order of MBM-TSSM is also found to increase.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209505","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056060
A. Shukla, Sumanta Gupta
The performance of free space optical (FSO) communication link is highly sensitive towards the intensity, phase and polarization fluctuations, which are induced by turbulent atmosphere. In order to study the impact of atmosphere induced turbulence on the optical signal, which propagates through it, it is essential to know the statistics of intensity, phase and polarization fluctuations. In this paper we report an experimental investigation that categorically measures the statistics of intensity and polarization fluctuations in terms of their probability density functions (PDFs) using a single setup of 210 cm link length and takes measurement under various turbulent conditions. Experimental results show that for all turbulent conditions considered in this paper log-normal and Gaussian distribution are closely matches with measured PDF for intensity and polarization angle fluctuations, respectively.
{"title":"Simultaneous Measurement of Atmospheric Turbulence Induced Intensity and Polarization Fluctuation for Free Space Optical Communication","authors":"A. Shukla, Sumanta Gupta","doi":"10.1109/NCC48643.2020.9056060","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056060","url":null,"abstract":"The performance of free space optical (FSO) communication link is highly sensitive towards the intensity, phase and polarization fluctuations, which are induced by turbulent atmosphere. In order to study the impact of atmosphere induced turbulence on the optical signal, which propagates through it, it is essential to know the statistics of intensity, phase and polarization fluctuations. In this paper we report an experimental investigation that categorically measures the statistics of intensity and polarization fluctuations in terms of their probability density functions (PDFs) using a single setup of 210 cm link length and takes measurement under various turbulent conditions. Experimental results show that for all turbulent conditions considered in this paper log-normal and Gaussian distribution are closely matches with measured PDF for intensity and polarization angle fluctuations, respectively.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130628352","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056091
Ananya Parameswaran, S. Raghavan
A quasi-elliptic stepped impedance low pass filter using modified dielectric is proposed in this work. The dielectric is modified using plated hole vias and is implemented with double layer topology. The pitch should be 0.22λg and the height of the artificial dielectric should be three times the other dielectric to result in quasi-elliptic characteristics. The presented technique resulted in transmission zero at the stop band edge of slow wave Butterworth filter with 78 dB attenuation and is higher compared to similar works reported. For proof of concept, the filter is fabricated and performance is validated with measurement. The simulated and measured results showed good mutual agreement with each other.
{"title":"Microstrip Quasi-Elliptic Low Pass Filter in Multilayer Topology","authors":"Ananya Parameswaran, S. Raghavan","doi":"10.1109/NCC48643.2020.9056091","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056091","url":null,"abstract":"A quasi-elliptic stepped impedance low pass filter using modified dielectric is proposed in this work. The dielectric is modified using plated hole vias and is implemented with double layer topology. The pitch should be 0.22λg and the height of the artificial dielectric should be three times the other dielectric to result in quasi-elliptic characteristics. The presented technique resulted in transmission zero at the stop band edge of slow wave Butterworth filter with 78 dB attenuation and is higher compared to similar works reported. For proof of concept, the filter is fabricated and performance is validated with measurement. The simulated and measured results showed good mutual agreement with each other.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132891147","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}
The verbal children affected with autism spectrum disorder (ASD) often shows some notable acoustic patterns. This paper represents the classification of autism speech, i.e., the speech signal of children affected with ASD. In addition, this work specifically aims to classify the speech signals of non-native Indo English speakers (children) affected with ASD. Previous studies, however, have focused only on native English speakers. Hence, for this study purpose a speech signal dataset of ASD children and a speech signal dataset of normal children were recorded in English, and all the children selected for the data collection were non-native Indo English speakers. Here, for the ASD and the normal children, the acoustic features explored for classification are namely, fundamental frequency (FO), strength of excitation (SoE), formants frequencies (F1 to F5), dominant frequencies (FD1, FD2), signal energy (E), zero-crossing rate (ZCR), mel-frequency cepstral coefficients (MFCC), and linear prediction cepstrum coefficients (LPCC). Further, these feature sets are classified by utilizing different classifiers. The KNN classifier model achieves the highest 96.5% accuracy with respect to other baseline models explored here.
{"title":"Acoustic Features Characterization of Autism Speech for Automated Detection and Classification","authors":"Abhijit Mohanta, Prerana Mukherjee, Vinay Kumar Mirtal","doi":"10.1109/NCC48643.2020.9056025","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056025","url":null,"abstract":"The verbal children affected with autism spectrum disorder (ASD) often shows some notable acoustic patterns. This paper represents the classification of autism speech, i.e., the speech signal of children affected with ASD. In addition, this work specifically aims to classify the speech signals of non-native Indo English speakers (children) affected with ASD. Previous studies, however, have focused only on native English speakers. Hence, for this study purpose a speech signal dataset of ASD children and a speech signal dataset of normal children were recorded in English, and all the children selected for the data collection were non-native Indo English speakers. Here, for the ASD and the normal children, the acoustic features explored for classification are namely, fundamental frequency (FO), strength of excitation (SoE), formants frequencies (F1 to F5), dominant frequencies (FD1, FD2), signal energy (E), zero-crossing rate (ZCR), mel-frequency cepstral coefficients (MFCC), and linear prediction cepstrum coefficients (LPCC). Further, these feature sets are classified by utilizing different classifiers. The KNN classifier model achieves the highest 96.5% accuracy with respect to other baseline models explored here.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131965587","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056051
P. Das
Incremental relaying (IR) has been widely studied in the cooperative communications literature in order to tradeoff between the improved signal-to-noise-ratio (SNR) and spatial diversity provided by a relay, and the additional time required by it to forward data to a destination. When multiple relays are present, several variants of IR with relay selection (RS) have been proposed and analyzed. These select one among the available relays to forward data only if the SNR of the source-to-destination (SD) link is either less than a threshold or less than the end-to-end SNR of at least one of the relays. However, an in-depth analysis of the average rate of the rate-optimal RS rule for IR, which turns out to be a non-linear function of the SNR of the SD link, and insights into its behavior are not available in the literature. We derive novel, closed-form expressions for this important performance metric. We further develop an insightful asymptotic analysis that helps to quantify the rate gain over direct transmission and characterizes the effect of various system parameters. We also extensively benchmark the performance of the rate-optimal RS rule against several IR variants proposed in the literature. We present numerical results to verify the analysis and show the impact of imperfect channel state information.
{"title":"Average Rate of Optimal Incremental Relaying with Selection: Analysis and Insights","authors":"P. Das","doi":"10.1109/NCC48643.2020.9056051","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056051","url":null,"abstract":"Incremental relaying (IR) has been widely studied in the cooperative communications literature in order to tradeoff between the improved signal-to-noise-ratio (SNR) and spatial diversity provided by a relay, and the additional time required by it to forward data to a destination. When multiple relays are present, several variants of IR with relay selection (RS) have been proposed and analyzed. These select one among the available relays to forward data only if the SNR of the source-to-destination (SD) link is either less than a threshold or less than the end-to-end SNR of at least one of the relays. However, an in-depth analysis of the average rate of the rate-optimal RS rule for IR, which turns out to be a non-linear function of the SNR of the SD link, and insights into its behavior are not available in the literature. We derive novel, closed-form expressions for this important performance metric. We further develop an insightful asymptotic analysis that helps to quantify the rate gain over direct transmission and characterizes the effect of various system parameters. We also extensively benchmark the performance of the rate-optimal RS rule against several IR variants proposed in the literature. We present numerical results to verify the analysis and show the impact of imperfect channel state information.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126893620","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9055995
Nitin Nageen, Subhashini, V. Bhatia
The paper presents FPGA implementation of turbo product code decoder with single and double error correcting BCH constituent codes that is capable of supporting high throughput and still maintains low complexity. The implementation is based on the Chase-Pyndiah algorithm, which exhibits a modular, simple structure with fine parallelism. Complexity reduction and pipelining for throughput and latency has been done through novel optimizations in submodules of TPC decoder. The resulting turbo decoder is implemented on a Xilinx Virtex-6 customized hardware. Performance comparison against third party IP cores is also presented,
{"title":"An Efficient FPGA implementation of Turbo Product Code decoder with single and double error correction","authors":"Nitin Nageen, Subhashini, V. Bhatia","doi":"10.1109/NCC48643.2020.9055995","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9055995","url":null,"abstract":"The paper presents FPGA implementation of turbo product code decoder with single and double error correcting BCH constituent codes that is capable of supporting high throughput and still maintains low complexity. The implementation is based on the Chase-Pyndiah algorithm, which exhibits a modular, simple structure with fine parallelism. Complexity reduction and pipelining for throughput and latency has been done through novel optimizations in submodules of TPC decoder. The resulting turbo decoder is implemented on a Xilinx Virtex-6 customized hardware. Performance comparison against third party IP cores is also presented,","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116090850","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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