Electromagnetic design of folded‐waveguide resonator filter with single finite‐frequency transmission zero

This article presents electromagnetic (EM) design of a new folded‐waveguide resonator filter that uses an advanced coupling scheme to achieve the desired asymmetric frequency response with an attenuation pole of finite frequency on the high side of the passband. A filter of this type with a fraction bandwidth about 5% at a center frequency of 4.45 GHz has been successfully designed using a commercially available electromagnetic simulator. The designed filter has been fabricated and tested. Simulations and experimental results are presented to validate the design and to show the advantages of this type of filter. This filter also provides a compact size compared with conventional cavity resonator filters. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Reduced graphene oxide supersonically sprayed on wearable fabric and decorated with iron oxide for supercapacitor applications

We demonstrate the fabrication of wearable supercapacitor electrodes.The electrodes were applied to wearable fabric by supersonically spraying the fabric with reduced graphene oxide(rGO)followed by decoration with iron oxide(Fe2O3)nanoparticles via a hydrothermal process.The integration of iron oxide with rGO flakes on wearable fabric demonstrates immense potential for applications in high-energy-storage devices.The synergetic impact of the intermingled rGO flakes and Fe2O3 nanoparticles enhances the charge transport within the composite electrode,ultimately improving the overall electrochemical performance.Taking advantage of the porous nature of the fabric,electrolyte diffusion into the active rGO and Fe2O3 materials was significantly enhanced and subsequently increased the electrochemical interfacial activities.The effect of the Fe2O3 concentration on the overall electrochemical performance was investigated.The optimal composition yields a specific capacitance of 360 F g-1 at a current density of 1A g-1 with a capacitance retention rate of 89％after 8500 galvanostatic cycles,confirming the long-term stability of the Fe2O3/rGO fabric electrode.     

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest (BO-RF) classifiers. This study uses four datasets (two collected from Indian hospitals—Ishita Pathology Center (IPC) of 4X, 10X, and 40X and Aster Medcity (AMC) of 10X, 20X, and 40X—two benchmark datasets—gland segmentation (GlaS) of 20X and IMEDIATREAT of 10X) comprising multiple microscope magnifications. Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy (97.25%-IPC, 94.40%-AMC, 97.58%-GlaS, 99.16%-Imediatreat), sensitivity (0.9725-IPC, 0.9440-AMC, 0.9807-GlaS, 0.9923-Imediatreat), specificity (0.9908-IPC, 0.9813-AMC, 0.9907-GlaS, 0.9971-Imediatreat) and F-score (0.9725-IPC, 0.9441-AMC, 0.9780-GlaS, 0.9923-Imediatreat). The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset, highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.     

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%–12% higher than the conventional technique.     

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person’s tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization techniques have already been introduced, although they still have certain drawbacks. In order to enhance feature extraction and classification in such a practical way, Mayfly optimization with Generative Adversarial Network (MayGAN) is introduced in this research. Furthermore, Generative Adversarial System (GAS) is integrated with Principal Component Analysis (PCA) in the feature-extracted model to classify the type of blood cancer in the data. The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia. Acute lymphocytic Leukaemia (ALL), acute myelogenous Leukaemia (AML), chronic lymphocytic Leukaemia (CLL), chronic myelogenous Leukaemia (CML), and aberrant White Blood Cancers (WBCs) are all successfully classified by the proposed MayGAN model. The proposed MayGAN identifies the abnormal activity in the WBC, considering the geometric features. Compared with the state-of-the-art methods, the proposed MayGAN achieves 99.8% accuracy, 98.5% precision, 99.7% recall, 97.4% F1-score, and 98.5% Dice similarity coefficient (DSC).     

GIS-Based Assessment for the Development of a Groundwater Quality Index Towards Sustainable Aquifer Management

This study aims at developing Groundwater Quality Indices (GQIs) that constitute a reliable tool in defining aquifer vulnerability. For this purpose, water quality sampling campaigns were conducted on 60 groundwater wells during most vulnerable periods of early and late summer to ensure the representativeness of the targeted GQI under worst case conditions. The samples were tested for various water quality indicators, which were then used to develop the GQIs through GIS-based mapping with spatial geostatistical analysis. The results contribute in filling a gap in GQI definition and form a basis for planning effective water quality management towards sustainable exploitation of groundwater resources particularly during summer periods when recharge is limited.     

A survey on routing algorithms for wireless Ad-Hoc and mesh networks

Wireless networking technology is evolving as an inexpensive alternative for building federated and community networks (relative to the traditional wired networking approach). Besides its cost-effectiveness, a wireless network brings operational efficiencies, namely mobility and untethered convenience to the end user. A wireless network can operate in both the “Ad-Hoc” mode, where users are self-managed, and the “Infrastructure” mode, where an authority manages the network with some Infrastructure such as fixed wireless routers, base stations, access points, etc. An Ad-Hoc network generally supports multi-hopping, where a data packet may travel over multiple hops to reach its destination. Among the Infrastructure-based networks, a Wireless Mesh Network (with a set of wireless routers located at strategic points to provide overall network connectivity) also provides the flexibility of multi-hopping. Therefore, how to route packets efficiently in wireless networks is a very important problem.A variety of wireless routing solutions have been proposed in the literature. This paper presents a survey of the routing algorithms proposed for wireless networks. Unlike routing in a wired network, wireless routing introduces new paradigms and challenges such as interference from other transmissions, varying channel characteristics, etc. In a wireless network, routing algorithms are classified into various categories such as Geographical, Geo-casting, Hierarchical, Multi-path, Power-aware, and Hybrid routing algorithms. Due to the large number of surveys that study different routing-algorithm categories, we select a limited but representative number of these surveys to be reviewed in our work. This survey offers a comprehensive review of these categories of routing algorithms.In the early stages of development of wireless networks, basic routing algorithms, such as Dynamic Source Routing (DSR) and Ad-Hoc On-demand Distance Vector (AODV) routing, were designed to control traffic on the network. However, it was found that applying these basic routing algorithms directly on wireless networks could lead to some issues such as large area of flooding, Greedy Forwarding empty set of neighbors, flat addressing, widely-distributed information, large power consumption, interference, and load-balancing problems. Therefore, a number of routing algorithms have been proposed as extensions to these basic routing algorithms to enhance their performance in wireless networks. Hence, we study the features of routing algorithms, which are compatible with the wireless environment and which can overcome these problems.     

Modeling Low Mach Number Reacting Flow with Detailed Chemistry and Transport

An efficient projection scheme is developed for the simulation of reacting flow with detailed kinetics and transport. The scheme is based on a zero-Mach-number formulation of the compressible conservation equations for an ideal gas mixture. It relies on Strang splitting of the discrete evolution equations, where diffusion is integrated in two half steps that are symmetrically distributed around a single stiff step for the reaction source terms. The diffusive half-step is integrated using an explicit single-step, multistage, Runge–Kutta–Chebyshev (RKC) method. The resulting construction is second-order convergent, and has superior efficiency due to the extended real-stability region of the RKC scheme. Two additional efficiency-enhancements are also explored, based on an extrapolation procedure for the transport coefficients and on the use of approximate Jacobian data evaluated on a coarse mesh. We demonstrate the construction in 1D and 2D flames, and examine consequences of splitting errors. By including the above enhancements, performance tests using 2D computations with a detailed C₁C₂ methane-air mechanism and a mixture-averaged transport model indicate that speedup factors of about 15 are achieved over the starting split-stiff scheme     

Computer-based interface for an integrated solid waste management optimization model

Planning a regional waste management strategy is a critical step that, if not properly addressed, will lead to an inefficient integrated solid waste management (ISWM) system. Regional planning affects the design, implementation, and efficiency of the overall ISWM scheme. Consequently, decision-makers must look for optimized regional waste management planning to achieve a successful strategy. The optimization of an ISWM strategy for an area requires the knowledge of available solid waste management alternatives and technologies, economic and environmental costs associated with these alternatives, and their applicability to the specific area. Decision-makers often have to rely on optimization models to examine the impacts of mass balance, capacity limitations, operation, and site availability as well as to analyze different alternative options in the selection of a cost effective, environmentally sound waste management alternative. In this context, the complexity associated with the formulation of optimization models may hinder its use, and consequently, user friendliness is a major concern. This paper presents an interface that was developed to address this concern, that is to formulate the matrices associated with an integrated waste management optimization model.     

Characterization of optical,thermal and electrical properties of SWCNTs/PMMA nanocomposite films

The optical, thermal and electrical behavior of single-wall carbon nanotubes (SWCNTs)/poly(methyl methacrylate) (PMMA) composite are studied as a function of SWCNTs concentration. The nanocomposites were prepared in the form of films by solution casting technique. The concentrations of SWCNTs in SWCNTs/PMMA films were 0, 0.5, 1, 1.5, 2, 3.5, 5, 7.5, and 10 wt%. High-resolution transmission electron microscopy showed that SWCNTs doped in PMMA is less fragmented as compared to the powder SWCNTs. This is due to the interactions with polymers as well as the fabrication method. X-ray diffraction patterns of SWCNTs/PMMA composite films indicated that there is no covalent interaction between SWCNTs and PMMA. In addition, it demonstrates a homogeneous dispersion of SWCNTs in PMMA matrix. The optical properties of SWCNTs/PMMA films of SWCNTs concentration from 0 to 2.0 wt% have shown that the absorption intensity of the composite was enhanced ≈8.5 times as compared to the plain PMMA. Photoacoustic spectroscopy technique was used as a powerful and non-destructive tool to determine the thermal diffusivity (α), thermal effusivity (e) and thermal conductivity (k). The composites exhibited ≈160 % improvement in k at 2.0 wt%. Furthermore, the DC electrical conductivity measurements of SWCNTs/PMMA showed that the percolation threshold value was about 2.0 wt% of SWCNTs loading.