Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.

The nature of alkanethiol self-assembled monolayer adsorption on sputtered gold substrates

A detailed study of the self-assembly and coverage by 1-nonanethiol of sputtered Au surfaces using molecular resolution atomic force microscopy (AFM) and scanning tunneling microscopy (STM) is presented. The monolayer self-assembles on a smooth Au surface composed predominantly of {111} oriented grains. The domains of the alkanethiol monolayer are observed with sizes typically of 5-25 nm, and multiple molecular domains can exist within one Au grain. STM imaging shows that the (4 × 2) superlattice structure is observed as a (3 × 2√3) structure when imaged under noncontact AFM conditions. The 1-nonanethiol molecules reside in the threefold hollow sites of the Au{111} lattice and aligned along its lattice vectors. The self-assembled monolayer (SAM) contains many nonuniformities such as pinholes, domain boundaries, and monatomic depressions which are present in the Au surface prior to SAM adsorption. The detailed observations demonstrate limitations to the application of 1-nonanethiol as a resist in atomic nanolithography experiments to feature sizes of ∼20 nm.

Formation of sub-7 nm feature size PS-b-P4VP block copolymer structures by solvent vapour process

The nanometer range structure produced by thin films of diblock copolymers makes them a great of interest as templates for the microelectronics industry. We investigated the effect of annealing solvents and/or mixture of the solvents in case of symmetric Poly (styrene-block-4vinylpyridine) (PS-b-P4VP) diblock copolymer to get the desired line patterns. In this paper, we used different molecular weights PS-b-P4VP to demonstrate the scalability of such high χ BCP system which requires precise fine-tuning of interfacial energies achieved by surface treatment and that improves the wetting property, ordering, and minimizes defect densities. Bare Silicon Substrates were also modified with polystyrene brush and ethylene glycol self-assembled monolayer in a simple quick reproducible way. Also, a novel and simple in situ hard mask technique was used to generate sub-7nm Iron oxide nanowires with a high aspect ratio on Silicon substrate, which can be used to develop silicon nanowires post pattern transfer.