Generic multimodal biometric fusion

Biometric systems utilize physiological or behavioral traits to automatically identify individuals. A unimodal biometric system utilizes only one source of biometric information and suffers from a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Multimodal biometrics refers to a system which utilizes multiple biometric information sources and can overcome some of the limitation of unimodal system. Biometric information can be combined at 4 different levels: (i) Raw data level; (ii) Feature level; (iii) Match-score level; and (iv) Decision level. Match score fusion and decision fusion have received significant attention due to convenient information representation and raw data fusion is extremely challenging due to large diversity of representation. Feature level fusion provides a good trade-off between fusion complexity and loss of information due to subsequent processing. This work presents generic feature information fusion techniques for fusion of most of the commonly used feature representation schemes. A novel concept of Local Distance Kernels is introduced to transform the available information into an arbitrary common distance space where they can be easily fused together. Also, a new dynamic learnable noise removal scheme based on thresholding is used to remove shot noise in the distance vectors. Finally we propose the use of AdaBoost and Support Vector Machines for learning the fusion rules to obtain highly reliable final matching scores from the transformed local distance vectors. The integration of the proposed methods leads to large performance improvement over match-score or decision level fusion

Methods and Applications of Multivariate Pattern Analysis in Functional MRI Data Analysis.

In spite of the tremendous advances in science and technology, the human brain and its functions are still not completely understood. Functional magnetic resonance imaging (fMRI) is an imaging modality that allows for non-invasive study of brain function and physiology. Thus, fMRI has found many applications in various fields involved in the study of cognition, psychology, psychiatry, neuroscience, etc. Machine learning techniques have gained tremendous interest in recent times for fMRI data analysis. These methods involve learning from numerous examples and then making predictions for new unseen examples. This work addresses the use of machine learning techniques to find and study multivariate patterns in the fMRI brain data. The two main applications explored in this work include temporal brain-state prediction and subject categorization. The within-subject brain-state prediction setup has been used to compare and contrast three different acquisition techniques in a motor-visual activation study. It has also been implemented to highlight the differences in pain regulation networks in healthy controls and subjects with temporomandibular disorders. Lastly, regression has been used to predict graded fMRI activation on a continuous scale in a motor activation and craving study. The between-subject categorization setup has been used to distinguish between patients with Asperger's disorder and healthy controls. A major contribution of our work involves a novel multi-subject machine learning framework. This technique helps to learn a model which is based on information acquired from multiple other subjects' data in addition to the subject's own data. This has been used to classify the craving and non-craving brain states of nicotine-dependent subjects, allowing examination of both population-wide as well as subject-specific neural correlates of nicotine craving. A real-time neurofeedback setup was implemented to provide feedback to a subject using their own brain activation data. Subjects can then be trained to self-regulate their own brain activation.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111357/1/ysshah_1.pd

Somewhat Homomorphic Encryption Technique with its Key Management Protocol

Cloud computing has been contemplated as the architecture of various Business organizations, providing easy access to vast data storage and applications services. Most of the cloud service providers encrypt the data only on the network , while some even store the data in encrypted format. This means anyone with access to the cloud servers (cloud service providers) can appropriate it. Even if the data is encrypted during storage, keys are often stored along with your data .Thus an end-to-end encryption scheme has been proposed as a promising solution to data storage on cloud ,in order to perform computations on the encrypted data and thereby store the key securely. Somewhat Homomorphic Encryption is a fully homomorphic encryption technique which is compact, semantically secure with significantly smaller public key and is capable of encrypting integer plaintexts rather than single bits, with comparatively lower expansion and computational complexities Keywords-Cloud computing, Cryptography, Homomorphic Key Management (HKM), Homomorphic encryption, Somewhat Homomorphic encryption(SHE)

Bridge Design between AXI Lite and AHB Bus Protocol

Architecture of bridge model between AXI Lite and AHB for this paper were simulated using Synopsys VCS and DC in Verilog HDL. Bridge structure mainly comprises of arbitration techniques, control signals, multiplexing techniques for writing data signals and Decoder for reading data section. In this work, bridge model between AHB and AXI lite was simulated and characterized. The proposed model of bridge design provides efficient communication between on chip bus protocols like AXI and AHB on chip in the era of deep sub-micron technology where channel side is reduced as much as 5 nm

Effect of polymer additives on transitioning and turbulent boundary layers

Injection of small quantities of polymer solutions in various internal and external turbulent flows can produce a substantial reduction of skin friction drag. This phenomenon, known as Toms phenomenon, has been actively studied in the past few decades and applied in various industrial flows to increase efficiency, decrease operating costs, and reduce emissions. A number of previous numerical studies have hypothesized that the long chains of polymer molecules interact with various turbulent motions thereby decreasing the turbulent fluctuations and reducing skin friction. The experimental investigations conducted in the present work provide a comprehensive understanding of the development of a polymer drag reduced boundary layer flow while providing critical insights into the polymer-turbulence interactions in both turbulent and bypass transitioning boundary layers. The outcomes are particularly applicable for the practical implementation of this flow control strategy on marine vehicles. The experiments are conducted in a specialized water tunnel facility by means of particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) in a flat-plate boundary layer injected with polymer solutions via a tangentially inclined two-dimensional slot. The drag reducing effect of the heterogeneously distributed polymers on the flow development is characterized by contrasting the results against the baseline flow of water in each case. Critical questions with regards to the effect of the polymer concentration on the drag reduction performance in turbulent boundary layers are first addressed by considering three different concentrations of polyethylene oxide (PEO) covering a wide range of the drag reduction regime. The analysis of velocity and concentration measurements provides a link between the local polymer concentration, flow development, and achieved drag reduction. The changes in the slope of the logarithmic region of the velocity profiles are associated with various sub-regimes of drag reduction providing insights into the dominance of the viscous and inertial effects within the respective sub-regimes, which are important for the understanding of the ultimate limit of drag reduction. Further investigations are conducted using three-dimensional PIV measurements in the buffer and lower log regions of a drag reduced boundary layer, which elucidate the effect of polymer injection on various coherent structures in this region. The polymers are noted to dampen the turbulence producing motions, such as ejections and sweeps, which is illustrated through conditionally averaged flow fields. Accordingly, the Reynolds shear stress, a measure of turbulence production, is observed to be reduced in both cores of the structures and around the quasi-streamwise vortices, presenting an effect expected due to various viscoelastic mechanisms. The same trend is also observed to varying degrees within other frequently occurring coherent structures which are associated with the low-speed streaks, such as hairpin-like vortices, meandered streaks, and the precursors of streak breakdown events, confirming the importance of the polymer-turbulence interactions in turbulence control with this technique. The detailed planar and tomographic PIV measurements are further utilized for the characterization of extreme skin friction events which are largely associated with the low and high-speed streaks. Conditional averaged flow fields corresponding to the extreme events elucidate the polymer effect on the associated topology of the near-wall flow surrounding these events while signifying the dampening of large structures of Reynolds shear stress formed within the buffer layer. Further, a scale decomposition based analysis elucidates the effect of polymers on various length scales which are directly associated with the reduction in the Reynolds shear stress. The scale-decomposed conditional flow fields are further utilized to establish a quantitative measure of the association of the near-wall Reynolds shear stress with the skin friction, highlighting the effect of polymer injection on the phase differences between these quantities. Considering the substantial effects of the polymers particularly on the turbulence producing coherent structures, the effect of polymers on the transitional-turbulent motions within a bypass transitioning boundary layer are investigated using both planar PIV and PLIF. The effect of polymer injection is observed to accelerate the transition process in comparison to the baseline Newtonian flow depending on the location of injection with respect to a trip-wire. The acceleration of the transition process is observed via the increase in the amplification of velocity perturbations in the early transition stages which are dominated by Kelvin-Helmholtz instabilities. Characterization of the resulting flow development illustrates important differences in the trends of flow statistics and skin friction, highlighting the advantages and drawbacks of polymer injection within the transitional regime of the boundary layer

Combined gene expression analysis in HIV Associated Dementia, Alzheimer’s disease and Parkinson’s disease- An in-silico approach

Human immunodeficiency virus (HIV) Type 1 infection predominantly affects the immune system. Nevertheless, scientific studies have proven its association with the Central Nervous system (CNS) causing several neurological complications leading to HIV Associated Dementia (HAD). HAD is characterized by a progressive, disabling decline in essential CNS functions such as cognition, motor control and behavior. These are the general characteristics of the most common Neuro degenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). The genetics of AD and PD is widely studied and clinical studies have shown cohesion in the pathology of HAD, AD and PD. Analysing the concurrent expression patterns of large number of genes amongst these related diseases will aid in establishing correlations between the genes and their functions.We have analysed the gene expression datasets of HAD, AD and PD from GEO database to determine the overlapping genes and transcription factors involved. The datasets were normalized using R-Bioconductor, and statistical analysis was performed to identify the significant genes using limma and related packages in R. Although substantial amount of common proteins among HAD and other Neuro degenerative diseases have been previously reported, our findings can help in expanding the pool of target genes and further enhancing the knowledge about the convergent pathways among HAD, AD and PD. These common markers identified will provide insights into parallel pathways of disease mechanisms and further assist in the understanding of progression of HAD pathogenesis

CMOS compatible metamaterial absorbers for hyperspectral medium wave infrared imaging and sensing applications

We experimentally demonstrate a CMOS compatible medium wave infrared metal-insulator-metal (MIM) metamaterial absorber structure where for a single dielectric spacer thickness at least 93% absorption is attained for 10 separate bands centred at 3.08, 3.30, 3.53, 3.78, 4.14, 4.40, 4.72, 4.94, 5.33, 5.60 μm. Previous hyperspectral MIM metamaterial absorber designs required that the thickness of the dielectric spacer layer be adjusted in order to attain selective unity absorption across the band of interest thereby increasing complexity and cost. We show that the absorption characteristics of the hyperspectral metamaterial structures are polarization insensitive and invariant for oblique incident angles up to 25° making them suitable for practical implementation in an imaging system. Finally, we also reveal that under TM illumination and at certain oblique incident angles there is an extremely narrowband Fano resonance (Q < 50) between the MIM absorber mode and the surface plasmon polariton mode that could have applications in hazardous/toxic gas identification and biosensing

Octave-spanning broadband absorption of terahertz light using metasurface fractal-cross absorbers

Synthetic fractals inherently carry spatially encoded frequency information that renders them as an ideal candidate for broadband optical structures. Nowhere is this more true than in the terahertz (THz) band where there is a lack of naturally occurring materials with valuable optical properties. One example are perfect absorbers that are a direct step toward the development of highly sought after detectors and sensing devices. Metasurface absorbers that can be used to substitute for natural materials suffer from poor broadband performance, while those with high absorption and broadband capability typically involve complex fabrication and design and are multilayered. Here, we demonstrate a polarization-insensitive ultrathin (∼λ/6) planar metasurface THz absorber composed of supercells of fractal crosses capable of spanning one optical octave in bandwidth, while still being highly efficient. A sufficiently thick polyimide interlayer produces a unique absorption mechanism based on Salisbury screen and antireflection responses, which lends to the broadband operation. Experimental peak absorption exceeds 93%, while the average absorption is 83% from 2.82 THz to 5.15 THz. This new ultrathin device architecture, achieving an absorption-bandwidth of one optical octave, demonstrates a major advance toward a synthetic metasurface blackbody absorber in the THz ban