Responses to Biotic and Abiotic Stresses and Transgenic Approaches in the Coffee Plant

Banavath Jayanna Naik,Seong-Cheol Kim,Min Ju Shin,Chun Whan Kim,Chan Kyu Lim,Hyun Joo An 한국국제농업개발학회 2019 韓國國際農業開發學會誌 Vol.31 No.4

Coffee (Coffea L.) belongs to the family Rubiaceae and is a main cash crop for tropical farmers. It has more quantity of medicinal value and protein (25–28%). As an environmentally sensitive crop, climate change has a significant impact on the quality and stable production of coffee. To develop abiotic baroreceptors on the tolerance response in coffee, tremendous research is going on to improve the productivity under varying degrees of stress at various growth stages. Physiological, morphological and biochemical parameters change the productivity and quality of coffee. This paper reviews some of the important aspects of biotic and abiotic tolerance in coffee. We explain the best implementation methods for the improvement of coffee production and briefly advantages or importance of coffee in medicinal values, significances of biotechnological aspects and genetic engineering approaches for crop improvements.

Coffee cultivation techniques, impact of climate change on coffee production, role of nanoparticles and molecular markers in coffee crop improvement, and challenges

Banavath Jayanna Naik,Seong-Cheol Kim,Ragula Seenaiah,Pinjari Akabar Basha,Eun Young Song 한국식물생명공학회 2021 JOURNAL OF PLANT BIOTECHNOLOGY Vol.48 No.4

Coffee is the most frequently consumed functional beverage world wide. The average daily coffee consumption is increasing. This crop, which plays an important role in the global economy is under great threat from climate change. To with stand the current climate change, farmers have to learn crop cultivation techniques, strategies to protect crops from diseases, and understand which type of seed varieties to use to avoid crop loss. The present review briefly discusses the coffee cultivation techniques, impact of climate changes on coffee production, processing techniques of coffee, and the importance of coffee in our society, including its che- mical composition and prevention against, major diseases. Furthermore, the importance and role of advanced nano- technology along with molecular approaches for coffee crop improvement and facing challenges are explained.

Speaker Verification with the Constraint of Limited Data

Kumari, Thyamagondlu Renukamurthy Jayanthi,Jayanna, Haradagere Siddaramaiah Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.4

Speaker verification system performance depends on the utterance of each speaker. To verify the speaker, important information has to be captured from the utterance. Nowadays under the constraints of limited data, speaker verification has become a challenging task. The testing and training data are in terms of few seconds in limited data. The feature vectors extracted from single frame size and rate (SFSR) analysis is not sufficient for training and testing speakers in speaker verification. This leads to poor speaker modeling during training and may not provide good decision during testing. The problem is to be resolved by increasing feature vectors of training and testing data to the same duration. For that we are using multiple frame size (MFS), multiple frame rate (MFR), and multiple frame size and rate (MFSR) analysis techniques for speaker verification under limited data condition. These analysis techniques relatively extract more feature vector during training and testing and develop improved modeling and testing for limited data. To demonstrate this we have used mel-frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) as feature. Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) are used for modeling the speaker. The database used is NIST-2003. The experimental results indicate that, improved performance of MFS, MFR, and MFSR analysis radically better compared with SFSR analysis. The experimental results show that LPCC based MFSR analysis perform better compared to other analysis techniques and feature extraction techniques.

Marangoni convection radiative flow of dusty nanoliquid with exponential space dependent heat source

Basavarajappa Mahanthesh,Bijjanal Jayanna Gireesha,Ballajja Chandra PrasannaKumara,Nagavangala Shankarappa Shashikumar 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.8

The flow of liquids submerged with nanoparticles is of significance to industrial applications, specificallyin nuclear reactors and the cooling of nuclear systems to improve energy efficiency. The application ofnanofluids in water-cooled nuclear systems can result in a significant improvement of their economicperformance and/or safety margins. Therefore, in this paper, Marangoni thermal convective boundarylayer dusty nanoliquid flow across a flat surface in the presence of solar radiation is studied. A two phasedusty liquid model is considered. Unlike classical temperature-dependent heat source effects, an exponentialspace-dependent heat source aspect is considered. Stretching variables are utilized to transformthe prevailing partial differential system into a nonlinear ordinary differential system, which is thensolved numerically via the Runge-Kutta-Fehlberg approach coupled with a shooting technique. The rolesof physical parameters are focused in momentum and heat transport distributions. Graphical illustrationsare also used to consider local and average Nusselt numbers. We examined the results under bothlinear and quadratic variation of the surface temperature. Our simulations established that the impact ofMarangoni flow is useful for an enhancement of the heat transfer rate.

Hierarchical Graph Based Segmentation and Consensus based Human Tracking Technique

Ramachandra, Sunitha Madasi,Jayanna, Haradagere Siddaramaiah,Ramegowda, Ramegowda Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.1

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

Combination of Classifiers Decisions for Multilingual Speaker Identification

Nagaraja, B.G.,Jayanna, H.S. Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

Effectiveness of Hall current and exponential heat source on unsteady heat transport of dusty TiO2-EO nanoliquid with nonlinear radiative heat

Basavarajappa Mahanthesh,Nagavangala Shankarappa Shashikumar,Bijjanal Jayanna Gireesha,Isac Lare Animasaun 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.4

The problem of exponential heat source across a flowing nanofluid (TiO2-EO; titanium oxide-Engine oil) containing tiny dust particles on a deformable planar plate has been an open question in meteorology. In this paper, the boundary layer transient two-phase flow of dusty nanoliquid on an isothermal plate which is deforming with time-dependent velocity in the presence of exponential heat source is studied. The impacts of Hall current, nonlinear radiative heat and an irregular heat source (temperature based heat source and exponential space-based heat source) are also accounted. Dusty nanofluid is the composition of dust particles and nanoliquid (TiO2-EO). Using relevant transformations, the system of PDEs is rehabil-itated to the system of ODEs and then treated numerically. Exploration of the impacts of pertinent param-eters on velocity and temperature fields is performed via graphical illustrations. Numeric data for skin friction factor and the Nusselt number is presented and their characteristics are analyzed/quantified through the slope of linear regression via data points.

Combination of Classifiers Decisions for Multilingual Speaker Identification

( B. G. Nagaraja ),( H. S. Jayanna ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.4

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMMUBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

Hierarchical Graph based Segmentation and Consensus based Human Tracking Technique

Sunitha Madasi Ramachandra,Haradagere Siddaramaiah Jayanna,Ramegowda 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.1

Accurate detection, tracking and analysis of human movement using robots and other visual surveillancesystems is still a challenge. Efforts are on to make the system robust against constraints such as variation inshape, size, pose and occlusion. Traditional methods of detection used the sliding window approach whichinvolved scanning of various sizes of windows across an image. This paper concentrates on employing a stateof-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation forcolor-consistent segments and object level segmentation for category-consistent regions. The tracking phase isachieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking schemewith validation phase. Localization of human region in each frame is performed by keypoints by casting votesfor the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-basedframework is used to detect voting behavior. The designed methodology is tested on the video sequences having3 to 4 persons.

Speaker Verification with the Constraint of Limited Data

( Thyamagondlu Renukamurthy Jayanthi Kumari ),( Haradagere Siddaramaiah Jayanna ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.4

Speaker verification system performance depends on the utterance of each speaker. To verify the speaker, important information has to be captured from the utterance. Nowadays under the constraints of limited data, speaker verification has become a challenging task. The testing and training data are in terms of few seconds in limited data. The feature vectors extracted from single frame size and rate (SFSR) analysis is not sufficient for training and testing speakers in speaker verification. This leads to poor speaker modeling during training and may not provide good decision during testing. The problem is to be resolved by increasing feature vectors of training and testing data to the same duration. For that we are using multiple frame size (MFS), multiple frame rate (MFR), and multiple frame size and rate (MFSR) analysis techniques for speaker verification under limited data condition. These analysis techniques relatively extract more feature vector during training and testing and develop improved modeling and testing for limited data. To demonstrate this we have used mel-frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) as feature. Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) are used for modeling the speaker. The database used is NIST-2003. The experimental results indicate that, improved performance of MFS, MFR, and MFSR analysis radically better compared with SFSR analysis. The experimental results show that LPCC based MFSR analysis perform better compared to other analysis techniques and feature extraction techniques.