Deep transfer modeling for classification of Maize Plant Leaf Disease

Multimedia Tools and Applications - Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently...     

Experience with Applying Formal Methods to Protocol Specification and System Architecture

In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel's processor families. In this paper we give an overview of some of the problems we have worked on, the results we have obtained, and the lessons we have learned. The last topic is addressed mainly from the perspective of platform architects.     

A new social and momentum component adaptive PSO algorithm for image segmentation

In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor.     

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.     

Two‐Dimensional Nanomaterials for Biomedical Applications: Emerging Trends and Future Prospects

Two‐dimensional (2D) nanomaterials are ultrathin nanomaterials with a high degree of anisotropy and chemical functionality. Research on 2D nanomaterials is still in its infancy, with the majority of research focusing on elucidating unique material characteristics and few reports focusing on biomedical applications of 2D nanomaterials. Nevertheless, recent rapid advances in 2D nanomaterials have raised important and exciting questions about their interactions with biological moieties. 2D nanoparticles such as carbon‐based 2D materials, silicate clays, transition metal dichalcogenides (TMDs), and transition metal oxides (TMOs) provide enhanced physical, chemical, and biological functionality owing to their uniform shapes, high surface‐to‐volume ratios, and surface charge. Here, we focus on state‐of‐the‐art biomedical applications of 2D nanomaterials as well as recent developments that are shaping this emerging field. Specifically, we describe the unique characteristics that make 2D nanoparticles so valuable, as well as the biocompatibility framework that has been investigated so far. Finally, to both capture the growing trend of 2D nanomaterials for biomedical applications and to identify promising new research directions, we provide a critical evaluation of potential applications of recently developed 2D nanomaterials.     

Effect of Colloidal Silver on Optical Transmittance Characteristics of Bulk Cadmium Zinc Telluride Crystals

Colloidal silver is observed to affect the transmittance of p-type Cd_1−y Zn _y Te (CZT) single-crystal substrate material at room temperature. The optical transmittance spectra have been analyzed in the near-infrared (NIR) and mid-infrared (MIR) regions. The transmittance characteristics of CZT showed significant reduction in absorption due to split-off valance band transitions in the NIR region and intervalence band absorption in the MIR region upon coating CZT substrates with silver paste. This reduction in absorption has been explained to be due to the compensation of the acceptor defects (native and foreign). Silver atoms incorporated from the silver coating help in compensation of these defects. A similar effect on transmittance characteristics of mercury cadmium telluride (MCT) epilayers grown on CZT substrates after coating silver paste on the CZT substrate side was also observed. An improvement in the transmittance of CZT substrates after the application of silver paste was observed. A similar improvement in transmittance is usually achieved by annealing the substrates in a Cd/Zn atmosphere. The results are explained by considering the formation of neutral complexes of acceptors (cadmium vacancies) and the interstitial silver. This study also points to the important conclusion that silver paste on CZT should be applied with caution for measurement purposes since it diffuses even at room temperature and modifies the optical characteristics.     

A meta-heuristic cuckoo search and eigen permutation approach for model order reduction

In this paper, a new model order reduction technique is presented by combining the benefits of the meta-heuristic cuckoo search optimization and Eigen permutation methods for order reduction of higher order continuous-time systems. In the proposed approach, the numerator and the denominator polynomials of reduced order model are determined by Cuckoo search and Eigen permutation approaches, respectively. The proposed approach preserves the stability of the original system into the lower order model as the Eigen permutation retains the dominant pole with simultaneous cluster formation of the remaining real and complex poles. The effectiveness of the proposed method is validated by single-input single-output and multiple-inputs multiple-outputs numerical examples.     

Method to improve geometry for heat transfer enhancement in PCM composite heat sinks

Use of composite heat sinks (CHS), constructed using a vertical array of ‘fins’ (or elemental composite heat sink, ECHS), made of large latent heat capacity phase change materials (PCM) and highly conductive base material (BM) is a much sought cooling method for portable electronic devices, which are to be kept below a set point temperature (SPT). This paper presents a thermal design procedure for proper sizing of such CHS, for maximizing the energy storage and the time of operation until all of the latent heat storage is exhausted.For a given range of heat flux, q″, and height, A, of the CHS, using a scaling analysis of the governing two dimensional unsteady energy equations, a relation between the critical dimension for the ECHS and the amount of PCM used (?) is determined. For a ?, when the dimensions of the ECHS are less than this critical dimension, all of the PCM completely melts when the CHS reaches the SPT. The results are further validated using appropriate numerical method solutions. A proposed correlation for chosen material properties yields predictions of the critical dimensions within 10% average deviation. However, the thermal design procedure detailed in this paper is valid, in general, for similar finned-CHS configurations, composed of any high latent heat storage PCM and high conductive BM combination.