Slow release nanofertilizers are of interest for reducing soil nutrient losses and preventing land degradation associated with established fertilizer use due to use of fertilizers. Chitosan nanoparticles (CN) were prepared following polymerization of chitosan with methacrylic acid and later incorporated with potassium (CNK). CN as well as CNK were characterized using Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy, field-emission scanning electron microscope, and atomic force microscopy (AFM) techniques. The slow potassium release property of CNK was elucidated employing membrane diffusion studies. Zea mays plant were tested in pot trials using different doses of K-formulation with potassium incorporated or otherwise and compared with suitable controls. Soils amended with reduced potassium rates (75% CNK) significantly increased the fresh-and drybiomass accumulation by 51 and 47%, respectively, in relation to positive control (100% KCl). The use of 75% CNK improved physical properties of soil by way of enhanced porosity, higher water conductivity, and enhanced friability that favoured root growth. These along with reduced dry density induced the plants to uptake higher quantities of nutrients and develop double the root biomass relative to control. Further, no deleterious effect of the nanoformulation was apparent and the treatments showed better carbon-cycling activity. Increased fluorescein diacetate (FDA) hydrolysis in these treatments demonstrated higher soil microbial activity in relation to control. CNK was hypothesized to condition the soil by cohesive bonding of soil particles, stabilizing the soil aggregates by coating them, and preventing their degradation amidst disruptive forces such as repeated watering. Sustained nutrient release synchronize with crop demands, reduced fertilizer requirement and increased productivity.

Abstract. Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization approach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the user's dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.

Abstract. The work proposes a hierarchical architecture for learning from dynamic scenes at various levels of knowledge abstraction. The raw visual information is processed at different stages to generate hybrid symbolic/sub-symbolic descriptions of the scene, agents and events. The background is incrementally learned at the lowest layer, which is used further in the mid-level for multi-agent tracking with symbolic reasoning. The agent/event discovery is performed at the next higher layer by processing the agent features, status history and trajectory. Unlike existing vision systems, the proposed algorithm does not assume any prior information and aims at learning the scene/agent/event models from the acquired images. This makes it a versatile vision system capable of performing in a wide variety of environments.