Fast splitting based tag identification algorithm for anti-collision in UHF RFID System

Efficient and effective objects identification using Radio Frequency Identification (RFID) is always a challenge in large scale industrial and commercial applications. Among existing solutions, the tree based splitting scheme has attracted increasing attention because of its high extendibility and feasibility. However, conventional tree splitting algorithms can only solve tag collision with counter value equals to zero and usually result in performance degradation when the number of tags is large. To overcome such drawbacks, we propose a novel tree-based method called Fast Splitting Algorithm based on Consecutive Slot Status detection (FSA-CSS), which includes a fast splitting (FS) mechanism and a shrink mechanism. Specifically, the FS mechanism is used to reduce collisions by increasing commands when the number of consecutive collision is above a threshold. Whereas the shrink mechanism is used to reduce extra idle slots introduced by FS. Simulation results supplemented by prototyping tests show that the proposed FSA-CSS achieves a system throughput of 0.41, outperforming the existing UHF RFID solutions

Secure Graph Database Search with Oblivious Filter

With the emerging popularity of cloud computing, the problem of how to query over cryptographically-protected data has been widely studied. However, most existing works focus on querying protected relational databases, few work has shown interests in graph databases. In this paper, we first investigate and summarize two single-instruction queries, namely Graph Pattern Matching (GPM) and Graph Navigation (GN). Then we follow their design intuitions and leverage secure Multi-Party Computation (MPC) to implement their functionalities in a privacy-preserving manner. Moreover, we propose a general framework for processing multi-instruction query on secret-shared graph databases and present a novel cryptographic primitive Oblivious Filter (OF) as a core building block. Nevertheless, we formalize the problem of OF and present its constructions using homomorphic encryption. Finally, we conduct an empirical study to evaluate the efficiency of our proposed OF protocol

Quantitative tissue pH measurement during cerebral ischemia using amine and amide concentration-independent detection (AACID) with MRI

Tissue pH is an indicator of altered cellular metabolism in diseases including stroke and cancer. Ischemic tissue often becomes acidic due to increased anaerobic respiration leading to irreversible cellular damage. Chemical exchange saturation transfer (CEST) effects can be used to generate pH-weighted magnetic resonance imaging (MRI) contrast, which has been used to delineate the ischemic penumbra after ischemic stroke. In the current study, a novel MRI ratiometric technique is presented to measure absolute pH using the ratio of CEST-mediated contrast from amine and amide protons: amine/amide concentration-independent detection (AACID). Effects of CEST were observed at 2.75 parts per million (p.p.m.) for amine protons and at 3.50 p.p.m. for amide protons downfield (i.e., higher frequency) from bulk water. Using numerical simulations and in vitro MRI experiments, we showed that pH measured using AACID was independent of tissue relaxation time constants, macromolecular magnetization transfer effects, protein concentration, and temperature within the physiologic range. After in vivo pH calibration using phosphorus ( 31P) magnetic resonance spectroscopy (31P-MRS), local acidosis is detected in mouse brain after focal permanent middle cerebral artery occlusion. In summary, our results suggest that AACID represents a noninvasive method to directly measure the spatial distribution of absolute pH in vivo using CEST MRI. © 2014 ISCBFM All rights reserved

Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) in rats at 9.4 Tesla

© 2019 McCunn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Purpose Neurite Orientation Dispersion and Density Imaging (NODDI) is a diffusion MRI (dMRI) technique used to characterize tissue microstructure by compartmental modelling of neural water fractions. Intra-neurite, extra-neurite, and cerebral spinal fluid volume fractions are measured. The purpose of this study was to determine the reproducibility of NODDI in the rat brain at 9.4 Tesla. Methods Eight data sets were successfully acquired on adult male Sprague Dawley rats. Each rat was scanned twice on a 9.4T Agilent MRI with a 7 ± 1 day separation between scans. A multi-shell diffusion protocol was implemented consisting of 108 total directions varied over two shells (b-values of 1000 s/mm2 and 2000 s/mm2). Three techniques were used to analyze the NODDI scalar maps: mean region of interest (ROI) analysis, whole brain voxel-wise analysis, and targeted ROI analyses (voxel-wise within a given ROI). The coefficient of variation (CV) was used to assess the reproducibility of NODDI and provide insight into necessary sample sizes and minimum detectable effect size. Results CV maps for orientation dispersion index (ODI) and neurite density index (NDI) showed high reproducibility both between and within subjects. Furthermore, it was found that small biological changes ( 50) for biological changes to be detected. Conclusions The ODI and NDI measured by NODDI in the rat brain at 9.4T are highly reproducible and may be sensitive to subtle changes in tissue microstructure