In this work, a novel approach is adopted to synthesize graphene oxide (GO) and reduced graphene oxide (rGO) from a natural precursor Desmostachya bipinnata (halfa grass). GO is synthesized by treating ash of halfa grass with aqueous and molten KOH. Further, reduced with lemon extract to synthesize rGO. Then GO and rGO active SERS substrates are fabricated with flexible adhesive tape. Structural and morphological features of SERS substrates are analyzed. Raman spectrum of GO and rGO consists of characteristic D and G peaks around 1331 and 1600 cm−1 and a 2D peak around 2700 cm−1. The variation in ID/IG ratio, decreasing inter‐planar spacing, and the redshift of UV – Vis absorption peak from 225 to 280 nm confirm the reduction of GO on lemon extract treatment. SERS activity of fabricated substrates is analyzed with agro pesticide thiabendazole (TBZ). The detection range of TBZ by SERS substrate lies between 1 mM to 1 nM with a minimum detection limit of 1 nM. This study emphasizes the use of GO synthesized from aqueous KOH treatment and reduced with lemon extract (rGO) for the development of highly sensitive, low cost and flexible SERS substrates to detect TBZ in various agricultural products.
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
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With the rapidly increasing amounts of data produced worldwide, networked and multi- user storage systems are becoming very popular. However, concerns over data security still prevent many users from migrating data to remote storage. The conventional solution is to encrypt the data before it leaves the owner’s premises. While sound from a security perspective,this approach prevents the storage provider from effectively applying storage efficiency functions, such as compression and deduplication, which would allow optimal usage of the resources and consequently lower service cost. Client-side data deduplication in particular ensures that multiple uploads of the same content only consume network bandwidth and storage space of a single upload. Deduplication is actively used by a number of backup providers as well as various data services. In this project, we present a scheme that permits the storage without duplication of multiple types of files. And also need the intuition is that outsourced data may require different levels of protection. Based on this idea, we design an encryption scheme that guarantees semantic security for unpopular data and provides weaker security and better storage and bandwidth benefits for popular data. This way, data deduplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. We can use the backup recover system at the time of blocking and also analyze frequent log in access system.
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