Fast design optimization of UWB antenna with WLAN Band-Notch

In this paper, a methodology for rapid design optimization of an ultra-wideband ( UWB) monopole antenna with a lower WLAN band-notch is presented. The band-notch is realized using an open loop resonator implemented in the radiation patch of the antenna. Design optimization is a two stage process, with the first stage focused on the design of the antenna itself, and the second stage aiming at identification of the appropriate dimensions of the resonator with the purpose of allocating the band-notch in the desired frequency range. Both optimization stages are realized using surrogate-based optimization involving variable-fidelity electromagnetic ( EM) simulation models as well as an additive response correction ( first stage), and sequential approximate optimization ( second stage). The final antenna design is obtained at the CPU cost corresponding to only 23 high-fidelity EM antenna simulations

(Achiral) Lefschetz fibration embeddings of 44-manifolds

In this paper, we prove Lefschetz fibration embeddings of achiral as well as simplified broken (achiral) Lefschetz fibrations of compact, connected, orientable $4$-manifolds over $D^2$ into the trivial Lefschetz fibration of $\mathbb CP^2\times D^2$ over $D^2$. These results can be easily extended to achiral as well as simplified broken (achiral) Lefschetz fibrations over $\mathbb CP^1.$ From this, it follows that every closed, connected, orientable $4$-manifold admits a smooth (simplified broken) Lefschetz fibration embedding in $\mathbb CP^2\times \mathbb CP^1.$ We provide a huge collection of bordered Lefschetz fibration which admit bordered Lefschetz fibration embeddings into a trivial Lefschetz fibration $\tilde\pi:D^4\times D^2\to D^2.$ We also show that every closed, connected, orientable $4$-manifold $X$ admits a smooth embedding into $S^4\times S^2$ as well as into $S^4\tilde\times S^2$. From this, we get another proof of a theorem of Hirsch which states that every closed, connected, orientable $4$-manifold smoothly embeds in $\mathbb R^7.$ We also discuss Lefschetz fibration embedding of non-orientable $4$-manifolds $X$, where $X$ does not admit $3$- and $4$-handles in the handle decomposition, into the trivial Lefschetz fibration of $\mathbb CP^2\times D^2$ over $D^2$.Comment: 38 Pages; 21 Figures; V2- We added results on Lefschetz fibration embedding of non-orientable manifolds. V3- We added result on smooth embedding of $4$-manifolds in $S^2\times S^4$ as well as in $S^2\tilde\times S^4$. V4- We added corollary which gives an embedding of $4$-manifolds in $\mathbb R^7$. V5, V6- Typos and some minor technical errors are corrected. V7- Theorems 29, 31, 32 are adde

Machine Learning based Classification of Diseased Mango Leaves

The preponderance of population depends on agriculture to produce crops which would be their primary subsistence for their livelihood. So, agriculture is considered the backbone of any nation. Mango (Mangifera indica Linn), belonging to a family Anacardiaceous, is a conspicuous fruit that captivates all ages because of its meticulous taste, delicious flavor, ampleness variety, and highly lustiness. Mangoes are generally rich in minerals, vitamins, fibers, and negotiable fat. Mango plants are exposed to many micro-organisms. If these are not detected and treated in the initial developing stages, it would affect peculiar parts of the mango plant and result in loss of overall productivity. Several factors like biotic and abiotic always ensue in the decrease in the overall productivity of mango plants. Self-regulated Detection of mango plant disease is imperative, and it must be detected at the preliminary stages of the growing period of the mango plant. This paper discusses the existing methodology to classify diseases in mango plant leaves by implementing ensemble technique (Stack) which includes algorithms like Decision Tree (DT), Support vector machine (SVM), Neural Network (NN), and Logistic Regression (LR). The developmental results validate that the disease classification methodology can successfully classify a higher percentage in predicting whether mango plant leaf is healthy or diseased.&nbsp

Cost-efficient modeling of antenna structures using Gradient Enhanced Kriging

Reliable yet fast surrogate models are indispensable in the design of contemporary antenna structures. Data-driven models, e.g., based on Gaussian Processes or support-vector regression, offer sufficient flexibility and speed, however, their setup cost is large and grows very quickly with the dimensionality of the design space. In this paper, we propose cost-efficient modeling of antenna structures using Gradient-Enhanced Kriging. In our approach, the training data set contains, apart from the EM-simulation responses of the structure at hand, also derivative data at the respective training locations obtained at little extra cost using adjoint sensitivity techniques. We demonstrate that introduction of the derivative information into the model allows for considerable reduction of the model setup cost (in terms of the number of training points required) without compromising its predictive power. The Gradient-Enhanced Kriging technique is illustrated using a dielectric resonator antenna structure. Comparison with conventional Kriging interpolation is also provided

Enhancement of Fatigue Strength on SAE 1541 Steel Link Plate with Slip Ball Burnishing Technique

This research paper describes a technique for the enhancement of the fatigue strength of the chain link plate in the drive system of a military armoured vehicle. SAE 1541 steel link plates of chains were subjected to cyclical tensile stress due to repeated loading and un-loading conditions. The crack was getting originated from the pitch hole and growth perpendicular to the chain pulling load, due to fatigue mechanism. In general plate holes are manufactured using the conventional process. An additional novel technique called the slip ball burnishing (SBB) method is applied for improving the hole properties. The improvement is made by producing local plastic deformation, improving surface finish and compressive residual stress throughout in the pierced hole. Both the conventional process (CP) and the SBB technique have been evaluated by optical, profile, surface roughness and micro harness tests. Experimental fatigue test validations were done in both chain samples using the Johnson-Goodman method. SBB chains passed 3x106 cycles at the load of 17.61 kN and CP chains passed 3x106 cycles at the load of 13.92 kN. The conclusion was that SBB made a significant improvement of 26.51 per cent of fatigue strength compared to CP

Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods

Reply to ``Comment on `Inverse exciton series in the optical decay of an excitonic molecule' "

As a reply to the Comment by I.S. Gorban {\it et al.} (Phys. Rev. B, preceding paper) we summarize our criticism on their claim of the first observation of the $M$ series in $\beta$-ZnP$_2$. We support our analysis by reporting the first observation of inverse {\it polariton} series from the excitonic molecules selectively generated at ${\bf K}_m \simeq {\bf 0}$ in a CuCl single crystal. This observation and its explanation within the bipolariton model complete our proof of the biexcitonic origin of the inverse series.Comment: The Comment by I.S. Gorban et al. has been rejecte

Security Methods in Internet of vehicles

The emerging wireless communication technology known as vehicle ad hoc networks (VANETs) has the potential to both lower the risk of auto accidents caused by drivers and offer a wide range of entertainment amenities. The messages broadcast by a vehicle may be impacted by security threats due to the open-access nature of VANETs. Because of this, VANET is susceptible to security and privacy problems. In order to go beyond the obstacle, we investigate and review some existing researches to secure communication in VANET. Additionally, we provide overview, components in VANET in details