Electrodeposited composites are gaining importance for their advantages including low cost, ease and simplicity of operation
to tailor made coatings for tribological applications. Generally, composites containing carbides (like SiC) are preferred
for high wear resistance along with increased hardness, improved corrosion resistance, and high temperature oxidation resistance
as compared to alloy and pure metal electroplating. In the present work, electrolytic codeposition technique was adopted in
the deposition of Ni-Co-SiC composite coating on mild steel substrate, using nickel alloyed with cobalt as the binder phase
with SiC as dispersed particles. To improve the properties of coating further, Cr plating was also performed. Since the particle
size and volume percent variation of dispersoid have great importance in codeposition, so the effect of these two variables
on the process of codeposition and properties was observed. Morphological studies of Ni-Co-SiC coating were carried out with
scanning electron microscopy and X-ray diffraction analysis to correlate the mechanical and corrosion behaviour of the coating. 相似文献
We propose a generalized form of optimal teleportation witness to demonstrate their importance in experimental detection of the larger set of entangled states useful for teleportation in higher dimensional systems. The interesting properties of our witness reveal that teleportation witness can be used to characterize mixed state entanglement using Schmidt numbers. Our results show that while every teleportation witness is also a entanglement witness, the converse is not true. Also, we show that a hermitian operator is a teleportation witness iff it is a decomposable entanglement witness. In addition, we analyze the practical significance of our study by decomposing our teleportation witness in terms of Pauli and Gell-Mann matrices, which are experimentally measurable quantities. 相似文献
Natural hazards such as flooding can cause changes in land-cover. The present study deals with the changes in land-cover in three worst affected districts (Anand, Vadodara and Kheda) of Gujarat state in India due to severe flood during 2005. The Indian Remote Sensing (IRS) P6 Linear Imaging Self Scanning (LISS) III satellite imageries of pre- and post-flooding periods were used as sources of information for the study area. Three classification approaches (unsupervised ISODATA, supervised Maximum Likelihood Classifier, and fuzzy rule based) were used to extract flood induced land-cover information. Results obtained from the above classification approaches were compared. Soft computing technique such as fuzzy based image classification gave better separability amongst classes as compared to hard classification techniques. The accuracy assessment showed that the fuzzy approach can predict land-cover more accurately than traditional approach and also showed great potential for dealing with mapping of flood induced land-cover. Unsupervised classification results for the period October 2004 to October 2005 revealed decrease in inland water bodies (14.49%) and agricultural area (6.42%) while increase in remaining land-cover. During February 2005 to February 2006, all land-cover classes decreased except agricultural fallow and sparse vegetation. In case of supervised classification, decreasing trend was observed only in case of agricultural area (6.78%) during October 2004 to October 2005. Similarly, during February 2005 to February 2006, increase in coastal water bodies (0.73%) and sparse vegetation (1.7%) was observed where as decreasing trend was noticed in the remaining land-cover classes. In fuzzy based classification, only decrease in agricultural area (7.09%) was observed from October 2004 to October 2005, whereas during February 2005 to February 2006, decrease in area was exhibited in all land-cover classes except coastal water bodies and sparse vegetation. Change detection indicated interchange of areas between inland and coastal water bodies and decrease in agricultural area leading to increase in area of agricultural fallow and sparse vegetation. 相似文献
A novel split-step finite-difference method for wide-angle beam propagation is presented. The formulation allows solution of the second-order scalar wave equation without having to make the slowly varying envelope and one-way propagation approximations. The method is highly accurate and numerically efficient requiring only simple matrix multiplication for propagation. 相似文献
Two-dimensional discrete cosine transforms are used in the core transformations in all profiles of the H.264/Advanced video coding (AVC) standard. In this paper, implementing the resource sharing of high throughput 4 × 4 and 8 × 8 forward and inverse integer transforms for high definition H.264 is presented. It is shown that the 4 × 4 forward/inverse transform can be obtained from 8 × 8 forward/inverse transform using selective data input and data arrangement at intermediate stages. Fast 8 × 8 forward and inverse transform is implemented using matrix decomposition and matrix operation such as Kronecker product and direct sum. The proposed implementation does not require any transpose memory and has a dual clocked pipeline structure. Compared with existing designs, the gate count is reduced by 27.7% in the proposed design. The maximum operating frequency of the proposed system is approx. 1.3 GHz, while the throughput is 7 G and 18.7 G pixels/s for 4 × 4 and 8 × 8 forward integer transforms, respectively. The proposed design can be used for real time H.264/AVC high definition processing owing to its high throughput and low hardware cost. 相似文献
Wireless Personal Communications - In the present scenario, there is a boom in the demand of the users to achieve increased capacity, high data, low latency, and high-performance rates. 5G New... 相似文献
Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.