Spectral analysis of atmospheric radar signal using filter banks — polyphase approach

This paper proposes a new non-parametric method to estimate the power spectral density of atmospheric radar signals using uniform filter banks through polyphase approach. The performance of proposed method is investigated for a simulated broadband and narrowband signals. This method reduces the variance without affecting the resolution and improves the signal to noise ratio significantly when compared to the existing method (periodogram/modified periodogram). The variance reduction is observed to be more in case of narrowband signals. The method has the computational cost similar to modified periodogram approach. This method is tested for practical atmospheric data collected at NARL, Gadanki, India, through the mesosphere–stratosphere–troposphere (MST) radar, back scattered echoes, in particular from high altitudes, which has low signal to noise ratio. Results have been validated using simultaneous Global Positioning System (GPS) Sonde data.     

Recycling of thermoplastic polystyrene waste using citrus peel extract for oil spill remediation

In this work, we report a low-cost, less energy intensive, and an innovative way of recycling thermoplastic polystyrene (PS) waste objects into submicron, aligned fibers using extract from citrus peel, an agricultural waste. As-fabricated recycled PS fabric is then structurally characterized and tested as an oil sorbent material. The hydrophobic-oleophilic PS fabric is found to absorb 40.5 ± 3.6 g/g of oil, with 77.3% oil retention within 1 h. To investigate the practical application of recycled PS fabric for oil spills remediation, we tested its buoyancy properties in oil-over-water static and dynamic system besides examining their reusability. The as-fabricated fabric floats on water after oil sorption indicating its high buoyancy and therefore can be collected easily after soaking the oil. This work is a simple illustration of systematic analysis of recycling two different waste materials (thermoplastic polystyrene and citrus peels) and reusing them into a more valuable product. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47886.     

Polymers from 2,5-difluoroterephthalic acid. I. Polyesters

A new synthetic route to 2,5-difluoroterephthalic acid (DFTA) was devised and from this several new series of polymers were prepared by polycondensation. This inaugural paper will cover polyesters while subsequent papers will report on keto polyethers, polyamides and other backbones derived from DFTA. DFTA was synthesized from 2,5-difluorotoluene, which was acylated and subsequently converted to the diacid by a several step oxidation process. Various polyesters were prepared by reacting it with numerous bisphenols and diols by solution condensation to give a series of difluoroterephthalate polyesters with viscosities ranging from 0.16 to 0.61 dl/g. Thermogravimetric analyses showed that the polyesters had thermal stabilities up to 480°C in nitrogen; melting temperatures ranged from 127 to 318°C.     

Antifeedant activity of some new heterocyclic compounds against the larvae of Spodoptera litura F

The antifeedant activity of diisoflavones a synthetic products is reported for the first time against S. litura. The antifeedant activity was tested employing the non-choice test method against the 4th instar pre-starved larvae.     

FD-DBN: Flow directed deep belief network for accurate anomaly detection in cloud computing

Security becomes the key concern in a cloud environment, as the servers are distributed throughout the globe and involve the circulation of highly sensitive data. Intrusions in the cloud are common because of the huge network traffic that paves the way for intruders to breach traditional security systems with sophisticated software. To avoid such problems, intrusion detection systems (IDSs) have been introduced by various researchers. Each IDS was developed to achieve a particular objective, that is, providing security by detecting intrusions. Most of the available IDS are inefficient and are unable to provide accurate classification. Also, some of them are computationally expensive to be implemented in practical scenarios. This article proposes a new and efficient IDS framework that can accurately classify the intrusion type through effective training to address the existing drawbacks. The proposed framework, named flow directed deep belief network (FD-DBN), involves three main phases: pre-processing, clustering, and classification. In pre-processing, certain data mining operations are carried out to clean the data. The clustering phase is carried out using the game-based k-means (GBKM) clustering algorithm. The clustered data is then provided as input to the FD-DBN classification framework, where the training process is carried out. The deep belief network (DBN) training is performed with dataset features, and the flow direction algorithm is adopted for tuning the weight parameters of DBN. Through tuning, the model yielded accurate classification outcomes. The simulations are done in Python 3.6, and the results proved that the proposed framework is much more effective than the existing IDS frameworks.     

Circuit Analysis and Optimization of GAA Nanowire FET Towards Low Power and High Switching

Silicon - The main aim of this work is to study the effect of symmetric and asymmetric spacer length variations towards source and drain on n-channel SOI JL vertically stacked (VS) nanowire (NW)...