Photofunctional Materials Based on Sheet Polymer Capped Organic Molecules for Visible‐Light‐Responsive Rewritable Paper

Journal of Inorganic and Organometallic Polymers and Materials - Historically, the recording of information over paper has always been regarded as the most important factor for maintaining records...     

Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera

Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient’s serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients’ treatment.     

Electrochemical degradation of waters containing O-Toluidine on PbO2 and BDD anodes

Electrochemical oxidation of O-Toluidine (OT) was studied by galvanostatic electrolysis using lead dioxide (PbO₂) and boron-doped diamond (BDD) as anodes. The influence of operating parameters, such as current density, initial concentration of OT and temperature was investigated. Measurements of chemical oxygen demand were used to follow the oxidation. The experimental data indicated that on PbO₂ and BDD anodes, OT oxidation takes place by reaction with electrogenerated hydroxyl radicals and is favoured by low current density and high temperature. Furthermore, BDD anodes offer significant advantages over PbO₂ in terms of current efficiency and oxidation rate.     

低k集成的曙光

随着对新化学品和集成方案了解的加深，我们不但可以减轻介电层孔隙率增加引起的负担还可以降低工艺对超低k薄膜的损伤。     

Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.     

A new real-time retinal tracking system for image-guided laser treatment

A new system is proposed for tracking sensitive areas in the retina for computer-assisted laser treatment of choroidal neovascularization (CNV). The system consists of a fundus camera using red-free illumination mode interfaced to a computer that allows real-time capturing of video input. The first image acquired is used as the reference image and utilized by the treatment physician for treatment planning. A grid of seed contours over the whole image is initiated and allowed to deform by splitting and/or merging according to preset criteria until the whole vessel tree is demarcated. Then, the image is filtered using a one-dimensional Gaussian filter in two perpendicular directions to extract the core areas of such vessels. Faster segmentation can be obtained for subsequent images by automatic registration to compensate for eye movement and saccades. An efficient registration technique is developed whereby some landmarks are detected in the reference frame then tracked in the subsequent frames. Using the relation between these two sets of corresponding points, an optimal transformation can be obtained. The implementation details of proposed strategy are presented and the obtained results indicate that it is suitable for real-time location determination and tracking of treatment positions.     

Design Guidelines for the Noise Optimization of a 0.18 μm CMOS Low-Noise Amplifier

This paper presents the design considerations for the noise optimization of fully integrated tuned low-noise amplifiers (LNA) based on the four noise parameters and two-port noise theory. Specifically, this paper provides the design guidelines for a 0.18 μm CMOS tuned LNA. These guidelines give a useful indication to the design tradeoffs associated with noise figure, power dissipation and gate overdrive voltage for the LNA designed using this technology. As a case study, a 10 GHz LNA has been designed using 0.18 μm CMOS technology for a wireless LAN application. The amplifier has a 2.4 dB noise figure with a −13 dBm third-order input intercept point, while drawing 5 mW from a 1.8 V power supply. The results show that the proposed theoretical contours of constant noise figure which relate the gate overdrive voltage and power dissipation can accurately predict the noise performance of a 0.18 μm CMOS LNA design Ahmed A. Youssef received the B.Sc. (Hon.) and M.Sc. degrees both in electrical engineering from Ain Shams University, Cairo, Egypt, in 1998 and 2002, respectively. Since 2003, he has been with the University of Calgary, AB, Canada, where he is currently working toward the Ph.D. degree in RF integrated circuits and systems. Mr. Youssef has joined the Wireless Research Center at TRLab, Alberta, Canada as a research associate in 2004. His research interests include the analog high speed integrated circuit for the wireless LAN applications. Mr. Youssef is the recipient of the Mobinil Telecommunication Inc. Pre-master Fellowship in 1999. He also received the Young Scientist award at the Maastricht General Assembly of the International Union of Radio Science in 2002 and an Honorable Mention at 2003 in the Symposium of the Microelectronics Research & Development in Canada, Montreal. Mr. Youssef received the Gordon Lewis Hedberg Doctoral Scholarship in 2005.     

Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Precedent and observation of performance are an essential part of the design and construction process in geotechnical engineering. For deep urban excavations designers rely on empirical data to estimate potential deformations and impact on surrounding structures. Numerical simulations are also employed to estimate induced ground deformations. Significant resources are dedicated to monitor construction activities and control induced ground deformations. While engineers are able to learn from observations, numerical simulations have been unable to fully benefit from information gained at a given site or prior excavation case histories in the same area. A novel analysis method, self-learning in engineering simulations (SelfSim), is introduced to integrate precedent into numerical simulations. SelfSim is an inverse analysis technique that combines finite element method, biologically inspired material models, and field measurements. SelfSim extracts relevant constitutive soil information from field measurements of excavation response such as lateral wall deformations and surface settlement. The resulting soil model, used in a numerical analysis, provides correct ground deformations and can be used in estimating deformations of similar excavations. The soil model can continuously evolve using additional field information. SelfSim is demonstrated using two excavation case histories in Boston and Chicago.     

Communication capabilities of product networks

Instead of providing separate solutions for each individual network, a unified theory is desirable to cover the study of a class of networks. Cartesian product graphs provide a common framework to investigate the performance of several individual networks. This paper addresses communication capabilities of product networks. Communication cost is generally characterized by the diameter, the average distance, the total number of paths, the traffic intensity, the saturation level, the queue length in each node, the communication delay and the network throughput. The diameter and average distance of product networks have been studied. However, no work has addressed the remaining measures for product networks. This paper presents a unified theory to evaluate the traffic intensity and the saturation level of product networks. We have theoretically computed the traffic intensity and the saturation level. Intensive simulations have been conducted to validate the analytical results and to compute the other measures for different workloads, different networks, and different network sizes. Examples of product networks that have been investigated are multidimensional meshes, multidimensional toruses, and r‐ary n‐cube networks. We have also shown that the structure (geometry) of a network is a primary factor for network high performance. For meshes and toruses, square networks present an optimal structure. While in case of an r‐ary n‐cubes, networks with higher radix outperform those with smaller radix. In particular, cross‐cubes (4‐ary n‐cube) are shown to perform better than binary cubes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Analysis of dynamic brain imaging data

Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.