Effective Aggregation and Querying of Probabilistic RFID Data in a Location Tracking Context

RFID applications usually rely on RFID deployments to manage high-level events such as tracking the location that products visit for supply-chain management, localizing intruders for alerting services, and so on. However, transforming low-level streams into high-level events poses a number of challenges. In this paper, we deal with the well known issues of data redundancy and data-information mismatch: we propose an on-line summarization mechanism that is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningfulness of the information. We also show that common information needs, i.e. detecting complex events meaningful to applications, can be effectively answered by executing temporal probabilistic SQL queries directly on the summarized data. All the techniques presented in this paper are implemented in a complete framework and successfully evaluated in real-world location tracking scenarios

RPDM: A System for RFID Probabilistic Data Management

Data streams are more and more commonly generated in a large number of scenarios by audio and video devices, Global Positioning System (GPS), Radio Frequency Identification (RFID) and other types of sensors. In particular, RFID technology has recently gained significant popularity, especially for real-time people and goods tracking, however the noisy, redundant and unreliable nature of RFID streams, coupled with their huge size, can make their exploitation and management difficult. In this paper, we present a realtime system for RFID Probabilistic Data Management (RPDM). The system manages unreliable and noisy raw RFID data and transforms them into reliable meaningful probabilistic data streams by means of a newly proposed method based on a probabilistic Hidden Markov Model (HMM). Moreover, to handle the huge data volume generated by RFID deployments, RPDM proposes and implements a simple on-line summarization mechanism, which is able to provide small space representation for the massive RFID probabilistic data streams while preserving the meaningful information. The results are promptly stored in a probabilistic database, in such a way that a wide range of probabilistic queries can be submitted and answered effectively. The experimental evaluation proves the feasibility of the approach in real-world object tracking scenarios

Data management techniques for active RFID applications

In the last several years, RFID technology has gained significant popularity due to its ability of de- tecting objects and people carrying small RFID tags in an environment equipped with RFID readers. This research involved the design, implementation and experimental evaluation of a realtime system that ad- dresses the above mentioned data management issues in the context of RFID location tracking systems

Fast On-Line Summarization of RFID Probabilistic Data Streams

Abstract. RFID applications usually rely on RFID deployments to manage high-level events. A fundamental relation for these purposes is the location of people and objects over time. However, the nature of RFID data streams is noisy, redundant and unreliable and thus streams of low-level tag-reads can be transformed into probabilistic data streams that can reach in practical cases the size of gigabytes in a day. In this paper, we propose a simple on-line summarization mechanism, which is able to provide small space representation for massive RFID probabilistic data streams while preserving the meaningful information. The main idea behind the proposed approach is to keep on aggregating tuples in an incremental way until a state transition is detected. Probabilistic tuples are processed as they arrive, hence avoiding the use of expensive offline disk based operations, and the output is stored in a probabilistic database in such a way that, as we also experimentally prove, a wide range of probabilistic queries can be applicable and answered effectively

Biosynthesis of precious metabolites in callus cultures of Eclipta alba

International audienceEclipta alba (False daisy) is an important medicinal plant with well-known antihepatotoxic activity. However, no previous in vitro studies are available for its callus culture for increased production of antioxidant secondary metabolites. Herein, we maintained a competent protocol for callus culture of E. alba using stem and leaf explants grown on MS medium containing various concentrations of thidiazuron, 6-benzylaminopurine (BAP) either alone or in association with α-naphthalene acetic acid (NAA). Among all the applied plant growth regulators, BAP along with NAA resulted in maximal dry biomass of 18.0 and 13.8 g/l for stem and leaf explants, respectively. Furthermore, the highest production of phenolics (375.7 mg/l for stem-associated callus and 298 mg/l for leaf-associated callus) and flavonoids (62.0 and 52.3 mg/l for stem- and leaf-associated callus, respectively) were found to be present in optimized callus culture. Antioxidant activity was also elucidated for both stem and leaf derived calli. The highest antioxidant activities (~ 93.5%) were witnessed for stem and leaf associated calli at set concentrations of 3.0 mg/l BAP + 1.0 mg/l NAA and 4.0 mg/l BAP, respectively. High-performance liquid chromatography analyses revealed optimum accumulation of coumarin (1.98 mg/g DW) and wedelolactone (49.63 mg/g DW) in leaf associated callus and desmethylwedelolactone (69.96 mg/g DW), β-amyrin (0.8179 mg/g DW) and eclalbatin (0.3202 mg/g DW) in stem associated callus at optimized concentration

UV-C mediated accumulation of pharmacologically significant phytochemicals under light regimes in in vitro culture of Fagonia indica (L.)

Abstract Fagonia indica (L.) is an important medicinal plant with multitude of therapeutic potentials. Such application has been attributed to the presence of various pharmacological important phytochemicals. However, the inadequate biosynthesis of such metabolites in intact plants has hampered scalable production. Thus, herein, we have established an in vitro based elicitation strategy to enhance such metabolites in callus culture of F. indica. Cultures were exposed to various doses of UV radiation (UV-C) and grown in different photoperiod regimes and their impact was evaluated on biomass accumulation, biosynthesis of phytochemicals along antioxidant expression. Cultures grown under photoperiod (16L/8D h) after exposure to UV-C (5.4 kJ/m2) accumulated optimal biomass (438.3 g/L FW; 16.4 g/L DW), phenolics contents (TPC: 11.8 μgGAE/mg) and flavonoids contents (TFC: 4.05 μgQE/mg). Similarly, HPLC quantification revealed that total production (6.967 μg/mg DW) of phytochemicals wherein kaempferol (1.377 μg/mg DW), apigenin (1.057 μg/mg DW), myricetin (1.022 μg/mg DW) and isorhamnetin (1.022 μg/mg DW) were recorded highly accumulated compounds in cultures at UV-C (5.4 kJ/m2) dose than other UV-C radiations and light regimes.. The antioxidants activities examined as DPPH (92.8%), FRAP (182.3 µM TEAC) and ABTS (489.1 µM TEAC) were also recorded highly expressed by cultures under photoperiod after treatment with UV-C dose 5.4 kJ/m2. Moreover, same cultures also expressed maximum % inhibition towards phospholipase A2 (sPLA2: 35.8%), lipoxygenase (15-LOX: 43.3%) and cyclooxygenases (COX-1: 55.3% and COX-2: 39.9%) with 1.0-, 1.3-, 1.3- and 2.8-fold increased levels as compared with control, respectively. Hence, findings suggest that light and UV can synergistically improve the metabolism of F. indica and could be used to produce such valuable metabolites on commercial scale

A Reasoning Engine for Intruders' Localization in Wide Open Areas using a Network of Cameras and RFIDs

Wide open areas represent challenging scenarios forsurveillance systems, since sensory data can be affected bynoise, uncertainty, and distractors. Therefore, the tasks oflocalizing and identifying targets (e.g., people) in such environmentssuggest to go beyond the use of camera-only deployments.In this paper, we propose an innovative systemrelying on the joint use of cameras and RFIDs, allowing usto “map” RFID tags to people detected by cameras and,thus, highlighting potential intruders. To this end, sophisticatedfiltering techniques preserve the uncertainty of dataand overcome the heterogeneity of sensors, while an evidentialfusion architecture, based on Transferable Belief Model,combines the two sources of information and manages conflictbetween them. The conducted experimental evaluationshows very promising results