Contrast enhancement of MRI images using morphological transforms and PSO

Medical imaging plays a crucial role in correct extraction of the significant information for monitoring the patient’s health and providing the quality treatment. A deluge of medical images requires initial interpretation for the presence of any abnormality, however, the correct diagnosis requires the images to be of good quality. To cope with the problem of poor contrast in medical images, this paper presents a method based on morphological transforms to improve the quality of the images. The proposed method incorporates Particle Swarm Optimization to find an optimum value of a parameter which controls the enhancement of the resulting image. The proposed algorithm is executed on a set of MRI images for testing its efficacy. The experimental results are compared in terms of both qualitative and quantitative parameters. The mean opinion score is obtained with the help of experts, which clearly shows the better performance of the proposed method. Furthermore, the parameters like Contrast Improvement Ratio, signal-to-noise ratio, peak signal-to-noise ratio, PL, and Structural Similarity Index are evident of better performance of proposed method when compared with the state-of-the-art methods and few recent methods. The comparison shows that the performance of the proposed method based on morphological transforms incorporating Particle Swarm Optimization is better not only visually but also in terms of other evaluation parameters.

     

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.     

Optimization of processing parameters of medium density fiberboard using response surface methodology for multiwalled carbon nanotubes as a nanofiller

In the present work, medium density fiberboard (MDF) panels were produced using multiwalled carbon nanotubes (MWCNT) reinforced urea formaldehyde resin. Response surface methodology was employed to optimize the relationship between the three variables, viz. pressing time, percentage of UF resin and percentage of MWCNT, used in the fabrication of MDF, and the influence of variables on the internal bonding (IB) and modulus of rupture (MOR) was studied. The optimum conditions based on the IB strength were determined as 8.18 % of UF resin, pressing time of 232 s, and MWCNT of 3.5 %. Similarly, the optimized conditions for MOR are also reported in this paper.     

Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

Transitive signatures: new schemes and proofs

We present novel realizations of the transitive signature primitive introduced by Micali and Rivest, enlarging the set of assumptions on which this primitive can be based, and also providing performance improvements over existing schemes. More specifically, we propose new schemes based on factoring, the hardness of the one-more discrete logarithm problem, and gap Diffie-Hellman (DH) groups. All these schemes are proven transitively unforgeable under adaptive chosen-message attack in the standard (not random-oracle) model. We also provide an answer to an open question raised by Micali and Rivest regarding the security of their Rivest-Shamir-Adleman (RSA)-based scheme, showing that it is transitively unforgeable under adaptive chosen-message attack assuming the security of RSA under one-more inversion. We then present hash-based modifications of the RSA, factoring, and gap Diffie-Hellman based schemes that eliminate the need for "node certificates" and thereby yield shorter signatures. These modifications remain provably secure under the same assumptions as the starting scheme, in the random oracle model.     

Interplay Between Superconductivity and Antiferromagnetism in HoNi2B2C

A microscopic theory of interplay between superconductivity and antiferromagnetism in rare-earth nickel boride, HoNi₂B₂C is developed from first principles. Self-consistent equations for the superconducting order parameter Δ and magnetic order parameter Γ are derived using a Green’s function technique and an equation of motion method. The theory is applied to explain the experimental results in the antiferromagnetic superconductor HoNi₂B₂C. The present model explains the true coexistence of superconductivity and antiferromagnetism in this system. The behavior of the superconducting order parameter (Δ), the magnetic order parameter (Γ), the specific heat, the density of states, the free energy and critical field (H _c) is also studied for the system HoNi₂B₂C. Distinct features of the coexistence region are discussed. There is the convincing evidence that the theory is fully compatible with the key experiments.     

Anatase-brookite mixed phase nano TiO2 catalyzed homolytic decomposition of ammonium nitrate

Compared to the conventional ammonium perchlorate based solid rocket propellants, burning of ammonium nitrate (AN) based propellants produce environmentally innocuous combustion gases. Application of AN as propellant oxidizer is restricted due to low reactivity and low energetics besides its near room temperature polymorphic phase transition. In the present study, anatase-brookite mixed phase TiO₂ nanoparticles (∼10 nm) are synthesized and used as catalyst to enhance the reactivity of the environmental friendly propellant oxidizer ammonium nitrate. The activation energy required for the decomposition reactions, computed by differential and non-linear integral isoconversional methods are used to establish the catalytic activity. Presumably, the removal of NH₃ and H₂O, known inhibitors of ammonium nitrate decomposition reaction, due to the surface reactions on active surface of TiO₂ changes the decomposition pathway and thereby the reactivity.     

UWB:个人无线局域网络的高速公路多频带OFDM VS.直接序列DS--两大技术特性的综合剖析

大约3年前,美国联邦通信委员会发布一份历史性的Report&Order报告,将3.1GHz～10.6GHz的7,500MHz频谱配置给超宽带(UltraWideBand,UWB)装置使用。这份报告为产业和学术界带来许多创新与技术进步的机会,业界也纷纷成立各种组织,例如多频带OFDM联盟(Multi-band OFDM Alliance)、UWB论坛以及IEEE802.15.3a特别工作小组,希望为UWB技术建立共同的标准。     

Statistical shape analysis: clustering, learning, and testing

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a minimum variance type criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.