Neural networks and blood cell identification

The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.     

Impulse breakdown voltages of triggered multistage vacuum gap

Several sealed-off triggered vacuum gaps are connected in series to improve hold-off voltage. The characteristics of impulse breakdown voltage of these series-connected gaps are investigated experimentally. The sum hold-off voltage of series-connected gaps decreases to a unit hold-off voltage when the maximum value of voltage division ratio across the gaps increases to unity. Self-breakdown probability of the series-connected gaps is always higher than that of a single gap under the same conditions. Hence, stage efficiency of the multistage gap decreases with increasing number of stages. Its value is 90 percent with 2-stage gap and 75 percent with 5-stage gap, respectively, under the same voltage division ratio and the same gap length (2.0 mm) in each stage. Triggered breakdown voltage of 2- or 3- stage gap is several hundred volts when all gaps are triggered simultaneously at the peak of the main impulse wave and a working voltage range is nearly 100 percent in this case. The working voltage range decreases with number of stages. Its value is 45 percent with 3-stage gap and 15 percent with 5-stage gap, respectively, when one triggered gap is fired for switching.     

Reversible parotid enlargement and pseudo-Sj?gren's syndrome secondary to hypertriglyceridemia

We describe 2 patients with a Sj?gren-like syndrome apparently secondary to hypertriglyceridemia. Both had bilateral parotitis in addition to musculoskeletal and sicca symptoms. Parotid gland histology revealed fatty infiltration with no inflammation. Therapy with dietary modification and triglyceride lowering drugs resulted in resolution of symptoms and parotid swelling in one patient. In the 2nd patient, hypertriglyceridemia was resistant to triglyceride lowering drugs, and parotid symptoms and swelling continued unabated. Our findings suggest aggressive treatment of hyperlipidemia in pseudo-Sj?gren's syndrome may result in amelioration of musculoskeletal and parotid symptoms.     

Perfect secret sharing scheme based on vertex domination set

Due to the fast development in data communication systems and computer networks in recent years, the necessity to protect the secret data has become extremely imperative. Several methods have been proposed to protect the secret data; one of them is the secret sharing scheme. It is a method of distributing a secret K among a finite set of participants, in such a way that only predefined subset of participant is enabled to reconstruct a secret from their shares. A secret sharing scheme realizing uniform access structure described by a graph has received a considerable attention. In this scheme, each vertex represents a participant and each edge represents a minimum authorized subset. In this paper, an independent dominating set of vertices in a graph G is introduced and applied as a novel idea to construct a secret sharing scheme such that the vertices of the graph represent the participants and the dominating set of vertices in G represents the minimal authorized set. While most of the previous schemes were based on the principle of adjacent vertices, the proposed scheme is based upon the principle of non-adjacent vertices. We prove that the scheme is perfect, and the lower bound of the information rate of this new construction is improved when compared to some well-known previous constructions. We include an experiment involving security threats to demonstrate the effectiveness of the proposed scheme.     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...     

Numerical method using cubic B-spline for the heat and wave equation

In the present paper, the cubic B-splines method is considered for solving one-dimensional heat and wave equations. A typical finite difference approach had been used to discretize the time derivative while the cubic B-spline is applied as an interpolation function in the space dimension. The accuracy of the method for both equations is discussed. The efficiency of the method is illustrated by some test problems. The numerical results are found to be in good agreement with the exact solution.     

Free and forced convection boundary layer flow through a porous medium with large suction

An analytical study is made of the free and forced convection boundary layer flow past a porous medium bounded by a semi-infinite vertical porous plate. Locally similar solutions are then obtained by a perturbation method for large suction. Solutions for the velocity and temperature distributions are shown graphically for various suction velocities and values of the driving parameter G_r/R, where G_r is the Grashof number and R_e is the Reynolds number. The corresponding values of the skin friction coefficient and the Nusselt number are finally shown in tabular form.     

Selective Recovery of Cadmium,Cobalt, and Nickel from Spent Ni–Cd Batteries Using Adogen® 464 and Mesoporous Silica Derivatives

Spent Ni–Cd batteries are now considered an important source for many valuable metals. The recovery of cadmium, cobalt, and nickel from spent Ni–Cd Batteries has been performed in this study. The optimum leaching process was achieved using 20% H₂SO₄, solid/liquid (S/L) 1/5 at 80 °C for 6 h. The leaching efficiency of Fe, Cd, and Co was nearly 100%, whereas the leaching efficiency of Ni was 95%. The recovery of the concerned elements was attained using successive different separation techniques. Cd(II) ions were extracted by a solvent, namely, Adogen^® 464, and precipitated as CdS with 0.5% Na₂S solution at pH of 1.25 and room temperature. The extraction process corresponded to pseudo-2nd-order. The prepared PTU-MS silica was applied for adsorption of Co(II) ions from aqueous solution, while the desorption process was performed using 0.3 M H₂SO₄. Cobalt was precipitated at pH 9.0 as Co(OH)₂ using NH₄OH. The kinetic and thermodynamic parameters were also investigated. Nickel was directly precipitated at pH 8.25 using a 10% NaOH solution at ambient temperature. FTIR, SEM, and EDX confirm the structure of the products.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.