Iris segmentation in non-ideal images using graph cuts

A non-ideal iris image segmentation approach based on graph cuts is presented that uses both the appearance and eye geometry information. A texture measure based on gradients is computed to discriminate between eyelash and non-eyelash regions, combined with image intensity differences between the iris, pupil, and the background (region surrounding the iris) are utilized as cues for segmentation. The texture and intensity distributions for the various regions are learned from histogramming and explicit sampling of the pixels estimated to belong to the corresponding regions. The image is modeled as a Markov Random Field and the energy minimization is achieved via graph cuts to assign each image pixel one of the four possible labels: iris, pupil, background, and eyelash. Furthermore, the iris region is modeled as an ellipse, and the best fitting ellipse to the initial pixel based iris segmentation is computed to further refine the segmented region. As a result, the iris region mask and the parameterized iris shape form the outputs of the proposed approach that allow subsequent iris recognition steps to be performed for the segmented irises. The algorithm is unsupervised and can deal with non-ideality in the iris images due to out-of-plane rotation of the eye, iris occlusion by the eyelids and the eyelashes, multi-modal iris grayscale intensity distribution, and various illumination effects. The proposed segmentation approach is tested on several publicly available non-ideal near infra red (NIR) iris image databases. We compare both the segmentation error and the resulting recognition error with several leading techniques, demonstrating significantly improved results with the proposed technique.     

Flexible amorphous‐silicon non‐volatile memory

Abstract— The development of a flexible, rewritable, non‐volatile memory (NVM) that is implemented on a standard, low‐temperature a‐Si:H process without additional mask steps is reported. This NVM is a part of a flexible‐display system. Each NVM cell is composed of differentially configured thin‐film‐transistors (TFTs). The cell reads out one of two stable states depending on the relative threshold voltages of the differentially configured TFTs. Information is stored in each cell by increasing the threshold voltage of one differential TFT or the other, utilizing the well‐known electrical‐stress degradation intrinsic to a‐Si:H TFTs. The stored information is retained indefinitely with no applied power. A test array of individually addressable NVM cells has been successfully fabricated and tested on flexible stainless‐steel substrates. Read and write operation, as well as preliminary reliability measurements, are described. The design is readily scalable to large memory arrays.     

Real-time motion segmentation of sparse feature points at any speed.

We present a real-time incremental approach to motion segmentation operating on sparse feature points. In contrast to previous work, the algorithm allows for a variable number of image frames to affect the segmentation process, thus enabling an arbitrary number of objects traveling at different relative speeds to be detected. Feature points are detected and tracked throughout an image sequence, and the features are grouped using a spatially constrained expectation-maximization (EM) algorithm that models the interactions between neighboring features using the Markov assumption. The primary parameter used by the algorithm is the amount of evidence that must accumulate before features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. Experimental results on a number of challenging image sequences demonstrate the effectiveness and computational efficiency of the technique.     

Synthesis and compressive behavior of Cr2GeC up to 48 GPa

M_n+1AX_n compounds have gathered huge momentum because of its exciting properties. In this paper we report the synthesis of ternary layered ceramic Cr₂GeC, a 211 M_n+1AX_n compound by hot-pressing. Scanning electron microscopy and X-ray diffraction have been employed to characterize the new synthesized phase. High-pressure compressibility of Cr₂GeC were measured using diamond anvil cell and synchrotron radiation at room temperature up to 48 GPa. No phase transformation was observed in the experimental pressure range. The bulk modulus of Cr₂GeC calculated using the Birch–Murnaghan equation of state is 169 ± 3 GPa, with K′ = 3.05 ± 0.15.     

Biocompatible tumor micro-environment responsive CS-g-PNIPAAm co-polymeric nanoparticles for targeted Oxaliplatin delivery

The objective of the research study was to develop and characterize a biodegradable, thermo and pH dual responsive Oxaliplatin-loaded chitosan-graft-poly-N-isopropylacrylamide (CS-g-PNIPAAm) co-polymeric nanoparticles as a tumor-targeting drug delivery system. CS-g-PNIPAAm co-polymers were synthesized, characterized and optimized its thermo and pH responsive properties for tumor microenvironment conditions. Optimized co-polymer could be efficiently loaded with Oxaliplatin in nanoparticle form, evaluated for their morphology (TEM), particle size, zeta potential, loading efficiency and drug content. In vitro drug release study at tumor microenvironment and physiological pH and temperature conditions. The in vitro drug release was optimal at above lower critical solution temperature (LCST) and tumor microenvironment pH when compared to physiological pH & temperature. MTT assay and fluorescence microscopic study showed that drug release and cell uptake was significantly enhanced in tumor microenvironment. In conclusion, the obtained nanoparticles appeared to be of great promise in tumor targeted drug delivery of oxaliplatin.     

Adaptive recurrent nonnegative matrix factorization with phase compensation for Single-Channel speech enhancement

The speech signals are affected by the background noise distortion that is unfavorable to both the intelligibility as well as the speech quality. Most of the speech processing algorithms function with the spectral magnitude without consideration of the spectral phase by leaving them unexplored and unstructured. The proposed single channel speech enhancement model called the Adaptive Recurrent Nonnegative Matrix Factorization (AR-NMF) is designed based on the phase compensation strategy with deep learning. The two major phases considered here are the training phase and the testing phase. During the process of training, the noisy speech signal is decomposed by the Hurst exponent-based Empirical Mode Decomposition (HEMD) and is converted into the frequency domain using Short Time Fourier Transform. Further, the new AR-NMF is used for denoising, where the tuning factor is optimally generated by the optimized RNN. Here, the hidden neurons are optimized using the proposed Adaptive Attack Power-based Sail Fish Optimization (AAP-SFO) with consideration of minimizing the Mean Absolute Error between the actual value and the predicted value. Finally, this phase compensated speech signal is given to the ISTFT that results in the final denoised clean speech signal. From the analysis, the CSED of AAP-SFO-AR-NMF for the street noise is 58.24%, 57.34%, 56.72%, and 77.37% more than RNMF, esHRNR, esTSNR, and Vuvuzela respectively. The performance of the proposed deep enhancement method is extensively evaluated and compared to diverse adverse noisy environments that describe the superiority of the proposed method.

     

Improved Connection Establishment of Dynamic Traffic with Queue in WDM Optical Networks

Wireless Personal Communications - Proper route selection between source and destination $$(s-d)$$ connection leads to efficient resource utilization which leads to the availability of resources...     

Microstructure of Molybdenum Disilicide-Silicon Carbide Nanocomposite Thin Films

Composite thin films of molybdenum disilicide-silicon carbide (MoSi₂-SiC) have been deposited via rf magnetron sputtering onto molybdenum substrates. An intermediate layer was deposited in the presence of nitrogen gas and evaluated as a potential diffusion barrier layer. The composite films have been characterized using X-ray diffractometry, scanning electron microscopy, transmission electron microscopy, and Auger electron spectroscopy. The as-deposited films were amorphous but crystallized into nanometer-sized grains after annealing under vacuum at 1000°C for 30 min. There was a significant amount of interdiffusion between the film and substrate, which resulted in the formation of subsilicides such as Mo₅Si₃ and MoSi₃, as well as Mo₂C. The films that were deposited via reactive sputtering in a nitrogen ambient were amorphous in both the as-deposited and annealed conditions. Significantly fewer second phases were detected with the presence of the intermediate layer, which suggests the potential use of the nitrided (MoSi _x N _y C _z ) layer as a high-temperature diffusion barrier layer for the silicon and carbon.     

Polymerization initiated with polymeric compounds,II

The polymerization of methyl methacrylate initiated by the copolymers methacrylaldehyde — styrene — divinylbenzene and acrylaldehyde — ethylene dimethacrylate in the absence of usual initiators was investigated. The polymerization was found to proceed fairly readily and fast. Acceleration can be achieved by adding glycerylaldehyde. An increase in the surface of the initiating copolymer favourably influences the reaction rate; at the same time, however, physical trapping of ungraft poly(methyl methacrylate) molecules in the macroporous initiator seems likely to occur. It was also found that only copolymers containing aldehyde groups could be used for initiation and that besides MMA some other monomers could be polymerized in this way, such as glycidyl methacrylate, acrylic and methacrylic acid, acrylonitrile, and alkyl acrylate.