Sparse Representation for Paddy Plants Nutrient Deficiency Tracking System

Moving object detection and tracking from consecutive frames of sensing devices (Unmanned Aerial Vehicles-UAV) needs efficient sampling from mass data with sufficient memory saving. Objects with super pixels are tracked by Compressive Sensing (CS) and the generative structural part model is designed to be adaptive to variation of deformable objects. CS can precisely reconstruct sparse signal with a small amount of sampling data. This system creates the sparse representation (SR) dictionary representing the nutrient deficiency tracking system for paddy plants to support the healthily growth of the whole field. This system uses compressed domain features that can be exploited to map the semantic features of consecutive frames. As the CS is a developing signal processing technique, a sparse signal is reconstructed with efficient sampling rate and creates the sparse dictionary. The SR for paddy plant health system can build rich information about paddy plants from signaling devices and can alert the deficiency conditions accurately in real time

Motion and emotion : Semantic knowledge for hollywood film indexing

Ph.DDOCTOR OF PHILOSOPH

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Computational Studies on Pharmaceutical Targets in Human Diseases

Bacterial multidrug resistance (i.e. the ability of some bacterial species to survive in presence of various drugs) has become a primary challenge at a global level. Due to various factors, such as the overuse of antibiotics in human activities like health care and farming or inadequate diagnostic, many bacteria have indeed evolved acquiring novel and highly efficient resistance mechanisms. Some species, in particular, have become resistant to almost all in-use drugs. Among the several mechanisms of resistance, efflux pumps of the RND superfamily (resistance-nodulation-cell division) play a major role. These complexes span the cell wall and are able to expel a wide range of noxious compounds, including antibiotics of many different classes. In order to reinvigorate the action of these drugs, a viable route is to hinder their transport out of the cell through co-administration of efflux pumps inhibitors (EPIs). At present several EPIs have been identified, but none of them is usable in clinical therapies due to adverse effects. Moreover, several questions are still open regarding the mode of action of known EPIs as well as the functioning mechanism of RND efflux pumps. Further research in this field is thus needed. In order to characterize the mode of action of several EPIs of this pump, we applied computational techniques such as molecular docking and molecular dynamics (MD) simulations. Specifically, we focused on the EPIs: (i) amitriptyline and chlorpromazine, repurposed drugs which were proven to act as inhibitors against AcrB; (ii) PAβN, a known inhibitor of the pump whose mode of action is not fully understood. This thesis focuses on the inhibition of the AcrB efflux pump, the best known representative of the RND superfamily. High-resolution structural data are indeed available for this protein (specifically, for its Escherichia coli orthologue). Moreover, a fluoroquinolone resistant variant of this pump has been detected in clinical environments. With regard to amitriptyline and chlorpromazine, our in silico investigations revealed that both compounds tend to occupy a known binding pocket of AcrB. Their binding mode presents considerable similarities with that of several substrates and other EPIs of the pump, indicating that amitriptyline and chlorpromazine may inhibit the AcrB pump through competitive binding. In the case of PAβN, MD simulations were compared with experimental data from hydrogen-deuterium exchange mass spectrometry. From these analyses, it emerged that PAβN can significantly restrain the conformational dynamics of AcrB and its fluoroquinolone resistant variant. This EPI, therefore, may act by preventing conformational changes that are functional for AcrB. Importantly, our MD simulations revealed that PAβN and the antibiotic ciprofloxacin can simultaneously occupy the same binding pocket, suggesting that the EPI does not act by competitive binding. Further computational analyses were conducted on structural models of Salmonella Typhimurium AcrB. Experimental structural data on this wt protein are indeed missing, while the structure of its fluoroquinolone resistant variant has recently been solved through cryo-electron microscopy (cryo-EM). In order to assess the structural differences between the two proteins, we derived their structural models through homology modelling and MD simulations (modeling of the fluoroquinolone resistant variant was integrated with cryo-EM data). Structural analyses were then performed, with focus on the binding pockets of the protein. Considerable differences were detected regarding the volume as well as the hydration properties of the pockets. Although not strictly related to EPI development, this information may be valuable for the design of novel drugs and/or inhibitors of AcrB from Salmonella

Molecular dynamics study of the allosteric control mechanisms of the glycolytic pathway

There is a growing body of interest to understand the regulation of allosteric proteins. Allostery is a phenomenon of protein regulation whereby binding of an effector molecule at a remote site affects binding and activity at the protein‟s active site. Over the years, these sites have become popular drug targets as they provide advantages in terms of selectivity and saturability. Both experimental and computational methods are being used to study and identify allosteric sites. Although experimental methods provide us with detailed structures and have been relatively successful in identifying these sites, they are subject to time and cost limitations. In the present dissertation, Molecular Dynamics Simulations (MDS) and Principal Component Analysis (PCA) have been employed to enhance our understanding ofallostery and protein dynamics. MD simulations generated trajectories which were then qualitatively assessed using PCA. Both of these techniques were applied to two important trypanosomatid drug targets and controlling enzymes of the glycolytic pathway - pyruvate kinase (PYK) and phosphofructokinase (PFK). Molecular Dynamics simulations were first carried out on both the effector bound and unbound forms of the proteins. This provided a framework for direct comparison and inspection of the conformational changes at the atomic level. Following MD simulations, PCA was run to further analyse the motions. The principal components thus captured are in quantitative agreement with the previously published experimental data which increased our confidence in the reliability of our simulations. Also, the binding of FBP affects the allosteric mechanism of PYK in a very interesting way. The inspection of the vibrational modes reveals interesting patterns in the movement of the subunits which differ from the conventional symmetrical pattern. Also, lowering of B-factors on effector binding provides evidence that the effector is not only locking the R-state but is also acting as a general heat-sink to cool down the whole tetramer. This observation suggests that protein rigidity and intrinsic heat capacity are important factors in stabilizing allosteric proteins. Thus, this work also provides new and promising insights into the classical Monod-Wyman-Changeux model of allostery

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Computational Modeling of Face-to-Face Social Interaction Using Nonverbal Behavioral Cues

The computational modeling of face-to-face interactions using nonverbal behavioral cues is an emerging and relevant problem in social computing. Studying face-to-face interactions in small groups helps in understanding the basic processes of individual and group behavior; and improving team productivity and satisfaction in the modern workplace. Apart from the verbal channel, nonverbal behavioral cues form a rich communication channel through which people infer – often automatically and unconsciously – emotions, relationships, and traits of fellowmembers. There exists a solid body of knowledge about small groups and the multimodal nature of the nonverbal phenomenon in social psychology and nonverbal communication. However, the problem has only recently begun to be studied in the multimodal processing community. A recent trend is to analyze these interactions in the context of face-to-face group conversations, using multiple sensors and make inferences automatically without the need of a human expert. These problems can be formulated in a machine learning framework involving the extraction of relevant audio, video features and the design of supervised or unsupervised learning models. While attempting to bridge social psychology, perception, and machine learning, certain factors have to be considered. Firstly, various group conversation patterns emerge at different time-scales. For example, turn-taking patterns evolve over shorter time scales, whereas dominance or group-interest trends get established over larger time scales. Secondly, a set of audio and visual cues that are not only relevant but also robustly computable need to be chosen. Thirdly, unlike typical machine learning problems where ground truth is well defined, interaction modeling involves data annotation that needs to factor in inter-annotator variability. Finally, principled ways of integrating the multimodal cues have to be investigated. In the thesis, we have investigated individual social constructs in small groups like dominance and status (two facets of the so-called vertical dimension of social relations). In the first part of this work, we have investigated how dominance perceived by external observers can be estimated by different nonverbal audio and video cues, and affected by annotator variability, the estimationmethod, and the exact task involved. In the second part, we jointly study perceived dominance and role-based status to understand whether dominant people are the ones with high status and whether dominance and status in small-group conversations be automatically explained by the same nonverbal cues. We employ speaking activity, visual activity, and visual attention cues for both the works. In the second part of the thesis, we have investigated group social constructs using both supervised and unsupervised approaches. We first propose a novel framework to characterize groups. The two-layer framework consists of a individual layer and the group layer. At the individual layer, the floor-occupation patterns of the individuals are captured. At the group layer, the identity information of the individuals is not used. We define group cues by aggregating individual cues over time and person, and use them to classify group conversational contexts – cooperative vs competitive and brainstorming vs decision-making. We then propose a framework to discover group interaction patterns using probabilistic topicmodels. An objective evaluation of ourmethodology involving human judgment and multiple annotators, showed that the learned topics indeed are meaningful, and also that the discovered patterns resemble prototypical leadership styles – autocratic, participative, and free-rein – proposed in social psychology