Defining sub-regions in locally sparsified compressive sensing MRI

Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.

Locally sparsified compressive sensing for improved MR image qualtity

The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.

Carbon Nanotube Nanoweb–Bioelectrode for Highly Selective Dopamine Sensing

A highly sensitive and selective dopamine sensor was fabricated with the unique 3D carbon nanotube nanoweb (CNT-N) electrode. The as-synthesised CNT-N was modified by oxygen plasma to graft functional groups in order to increase selective electroactive sites at the CNT sidewalls. This electrode was characterized physically and electrochemically using HRSEM, Raman, FT-IR, and cyclic voltammetry (CV). Our investigations indicated that the O2-plasma treated CNT-N electrode could serve as a highly sensitive biosensor for the selective sensing of dopamine (DA, 1 μM to 20 μM) in the presence of ascorbic acid (AA, 1000 μM).

The Nitric Oxide (NO)-Sensing Repressor NsrR of Neisseria meningitidis Has a Compact Regulon of Genes Involved in NO Synthesis and Detoxification

We have analyzed the extent of regulation by the nitric oxide (NO)-sensitive repressor NsrR from Neisseria meningitidis MC58, using microarray analysis. Target genes that appeared to be regulated by NsrR, based on a comparison between an nsrR mutant and a wild-type strain, were further investigated by quantitative real-time PCR, revealing a very compact set of genes, as follows: norB (encoding NO reductase), dnrN (encoding a protein putatively involved in the repair of nitrosative damage to iron-sulfur clusters), aniA (encoding nitrite reductase), nirV (a putative nitrite reductase assembly protein), and mobA (a gene associated with molybdenum metabolism in other species but with a frame shift in N. meningitidis). In all cases, NsrR acts as a repressor. The NO protection systems norB and dnrN are regulated by NO in an NsrR-dependent manner, whereas the NO protection system cytochrome c′ (encoded by cycP) is not controlled by NO or NsrR, indicating that N. meningitidis expresses both constitutive and inducible NO protection systems. In addition, we present evidence to show that the anaerobic response regulator FNR is also sensitive to NO but less so than NsrR, resulting in complex regulation of promoters such as aniA, which is controlled by both FNR and NsrR: aniA was found to be maximally induced by intermediate NO concentrations, consistent with a regulatory system that allows expression during denitrification (in which NO accumulates) but is down-regulated as NO approaches toxic concentrations.

Sensing the story: structure and improvisation in writing for performance.

This article discusses the process of writing through devising in performance. It takes as a case study Apart of and A Part From in which muscle and sense memory – touch, smell, taste, sound and sight – were the points of departure for a contemporary performance piece on migration and identity. This was a practice-as-research project aimed to better understand the artist’s body (myself) in improvisation with memory fragments. Working from a non-verbal frame the writing experience began as actions in space, then new memories in my body, to maps on paper, to key word and some staging patterns, to systematic capturing of the textual, rhythmic and spatial structures in the emerging 30-minute performance. Various objects were incorporated to give aesthetic coherence of the piece. The article reports on the processes of writing in action, the writing and performing as lived experience and how the writing emerged from the spaces in between – self and other, self and object, self and space, present self and past self.

Participatory sensing and education: helping the community mitigate sleep disturbance from traffic noise

Purpose – This paper aims to present a project in Australia, where participants use smartphones to measure the level of traffic noise in their homes. Through the data collected, participants learn if they are subjected to sleep disturbances and, if so, understand how they can manage the issue to protect their health. The project also has a secondary purpose: the local council would like to engage its community through the exercise and be seen as acting on the community’s problems.

Monitoring protected areas using remote sensing technology

Due to irrational use of natural resources, human society is facing unprecedented threats. Remote sensing is one of the essential tools to determine changes in various forms of biological diversity over time. There are many methods to determine changes in protected areas, using satellite images. In this paper after introducing different change detection methods and their advantages and disadvantages, a hybrid method is used to analyse changes in forests and protected areas in a national park. Two Landsat images of Golestan National Park in Iran (taken in 1998 and 2010) were used. This hybrid approach combines Change Vector Analysis (CVA) for flagging the occurrence of changes, followed by signature extension to assign labels to changedpixels. The main objective of this paper is to propose a method for discovering and assessing environmental threats to natural treasures.

Synergistic effect of multi walled carbon nanotubes and reduced graphene oxides in natural rubber for sensing application

Utilizing the electrical properties of polymer nanocomposites is an important strategy to develop high performance solvent sensors. Here we report the synergistic effect of multi walled carbon nanotubes (MWCNTs) and reduced graphene oxide (RGO) in regulating the sensitivity of the naturally occurring elastomer, natural rubber (NR). Composites were fabricated by dispersing CNTs alone and together with exfoliated RGO sheets (thermally reduced at temperatures of 200 and 600 °C) in NR by a solution blending method. RGO exfoliation and the uniform distribution of fillers in the composites were studied by atomic force microscopy, Fourier transformation infrared spectroscopy, X-ray diffraction, transmission electron microscopy and Raman spectroscopy. The solvent sensitivity of the composite samples was noted from the sudden variation in electrical conductivity which was due to the breakdown of the filler networks during swelling in different solvents. It was found that the synergy between CNTs and RGO exfoliated at 200 °C imparts maximum sensitivity to NR in recognizing the usually used aromatic laboratory solvents. Mechanical and dynamic mechanical studies reveal efficient filler reinforcement, depending strongly on the nature of filler-elastomer interactions and supports the sensing mechanism. Such interactions were quantitatively determined using the Maier and Göritz model from Payne effect experiments. It is concluded that the polarity induced by RGO addition reduces the interactions between CNTs and ultimately results in the solvent sensitivity. © 2013 The Royal Society of Chemistry.

Molecularly engineered graphene surfaces for sensing applications: A review.

Graphene is scientifically and commercially important because of its unique molecular structure which is monoatomic in thickness, rigorously two-dimensional and highly conjugated. Consequently, graphene exhibits exceptional electrical, optical, thermal and mechanical properties. Herein, we critically discuss the surface modification of graphene, the specific advantages that graphene-based materials can provide over other materials in sensor research and their related chemical and electrochemical properties. Furthermore, we describe the latest developments in the use of these materials for sensing technology, including chemical sensors and biosensors and their applications in security, environmental safety and diseases detection and diagnosis.

Image encryption based on compressed sensing and blind source separation

A novel image encryption scheme based on compressed sensing and blind source separation is proposed in this work, where there is no statistical requirement to plaintexts. In the proposed method, for encryption, the plaintexts and keys are mixed with each other using a underdetermined matrix first, and then compressed under a project matrix. As a result, it forms a difficult underdetermined blind source separation (UBSS) problem without statistical features of sources. Regarding the decryption, given the keys, a new model will be constructed, which is solvable under compressed sensing (CS) frame. Due to the usage of CS technology, the plaintexts are compressed into the data with smaller size when they are encrypted. Meanwhile, they can be decrypted from parts of the received data packets and thus allows to lose some packets. This is beneficial for the proposed encryption method to suit practical communication systems. Simulations are given to illustrate the availability and the superiority of our method.