Purdue University

About: Purdue University is a education organization based out in West Lafayette, Indiana, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 73219 authors who have published 163563 publications receiving 5775236 citations. The organization is also known as: Purdue & Purdue-West Lafayette.

Tunable lifetime multiplexing using luminescent nanocrystals

Abstract: Optical multiplexing plays an important role in applications such as optical data storage1, document security2, molecular probes3,4 and bead assays for personalized medicine5. Conventional fluorescent colour coding is limited by spectral overlap and background interference, restricting the number of distinguishable identities. Here, we show that tunable luminescent lifetimes τ in the microsecond region can be exploited to code individual upconversion nanocrystals. In a single colour band, one can generate more than ten nanocrystal populations with distinct lifetimes ranging from 25.6 µs to 662.4 µs and decode their well-separated lifetime identities, which are independent of both colour and intensity. Such ‘τ-dots’ potentially suit multichannel bioimaging, high-throughput cytometry quantification, high-density data storage, as well as security codes to combat counterfeiting. This demonstration extends the optical multiplexing capability by adding the temporal dimension of luminescent signals, opening new opportunities in the life sciences, medicine and data security. Control over the luminescence lifetimes of upconversion nanocrystals allows a new form of temporal multiplexing for imaging and data-storage applications.

Tillage systems and soil ecology

Abstract: Tillage systems affect the soil physical and chemical environment in which soil organisms live, thereby affecting soil organisms. Tillage practices change soil water content, temperature, aeration, and the degree of mixing of crop residues within the soil matrix. These changes in the physical environment and the food supply of the organisms affect different groups of organisms in different ways. One of the challenges of research in soil ecology is to understand the impacts of management on the complex interactions of all organisms at the soil community level. In addition to the response of organisms to soil manipulations, agriculturalists are interested in the actions of soil organisms on the physical and chemical environment in the soil. Soil organisms perform important functions in soil, including structure improvement, nutrient cycling, and organic matter decomposition. This paper discusses the effects of tillage practices on soil organism populations, functions, and interactions. Although there is a wide range of responses among different species, most organism groups have greater abundance or biomass in no-till than in conventional tillage systems. Larger organisms in general appear to be more sensitive to tillage operations than smaller organisms, due to the physical disruption of the soil, burial of crop residue, and the change in soil water and temperature resulting from residue incorporation. Variations in responses found in different studies reflect different magnitudes of tillage disruption and residue burial, timing of the tillage operations, timing of the measurements, and different soil, crop, and climate combinations. The paper concludes with a discussion of challenges for tillage researchers.

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

Abstract: Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to express the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. Then, we reformulate the algorithm in a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Evaluating probabilistic queries over imprecise data

Abstract: Many applications employ sensors for monitoring entities such as temperature and wind speed. A centralized database tracks these entities to enable query processing. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), it is often infeasible to store the exact values at all times. A similar situation exists for moving object environments that track the constantly changing locations of objects. In this environment, it is possible for database queries to produce incorrect or invalid results based upon old data. However, if the degree of error (or uncertainty) between the actual value and the database value is controlled, one can place more confidence in the answers to queries. More generally, query answers can be augmented with probabilistic estimates of the validity of the answers. In this paper we study probabilistic query evaluation based upon uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.