K. J. Somaiya Institute of Engineering and Information Technology

This paper presents the deployment of Runge-Kutta method to overcome the main challenge in analysis of failure of aircraft wing structure subjected to wind pressure and point loading. Failure phenomenon of any structure is time dependents and is typically referred as dynamic in engineering mechanics and is fairly a complex to investigate. In this context, the dynamic analysis concept has successfully implemented by using computer program in SCILAB software. The numerical technique is adopted as Runge-Kutta fourth order (RK4) method for performing dynamic behaviour of wind structure. The demonstration of failure mode of wing structure is based on function of time. The numerical approach is deemed to provide a detailed description of these phenomena affecting the overall dynamic of failure envelope of wing structure. The parametric study is presented; likewise the effect on failure of wing structure by changing different wind pressure, length and moment, respectively. The wing structure is analytically validated against available literature. Finally, others important failure results obtained from this analysis has discussed in detail.

The internet has unlocked a whole new universe. It has no bounds and provides individuals with tremendous economic prospects all throughout the world. People can live better lives as a result of it. The internet has become one of the most important channels for communication. It has caused a massive range of information to be available online. This in turn has led to a lot of new threats coming into play, making it hard for network security to find breaches. An intrusion detection system (IDS) is a technology that scans network activity for unusual behavior and sends out alerts when it is found. It still has trouble detecting new intrusions, increasing the detection's precision and lowering false alert rates, despite the enormous efforts of the researchers. This research paper begins with a quick overview of IDS and its forms. We then go over several AI-based methods for Network-based IDS (NIDS), contrasting their advantages and disadvantages, while also determining evaluation metrics for each. Further discussing the various datasets used. We conclude our research by listing the research challenges along with the current and future trends.

The Groundstation is a very crucial part of any kind of space mission as it is the primary source of communication with satellites. The Groundstation system needs to be robust and reliable to avoid communication failure and it is the reason why we need to use space-grade components in such a system which increases the cost very high beyond the capacity of amateur space enthusiasts and small academic institutes. The aim is to create a fully automated Ground Station system using COTS components. In this paper, we present the Groundstation system that is composed of four subsystems–Antenna, Antenna Rotator, Transceiver System, and Data Management System. The Antenna works in the 2 m VHF Amateur Radio Band which is the expected spectrum of frequency allocation by the IARU for BeliefSat-1. This directional Antenna is hosted on a Single-axis Antenna Rotator which is used to point the antenna in the direction of the Satellite. It is controlled by a Transceiver System. The Transceiver System is based on the Software defined radio along with dire-wolf software to decode the data. The data received from the Satellite is stored on the Server. The Data Management System comprises a Server and a Web-based GUI. The transceiver is connected to the Data Management System using the Internet. The Web-based GUI is used to remotely control the operations of the Groundstation and to display the Satellite data.

With the advent of technology and increase in ease of access to digital devices, there has been a burgeoning increase in the flow and creation of information all across the internet. However, this has led to a lot of saturation and an increased amount of redundant information, which needs to be sorted through manually many times to find the important or pertinent information. But this ends up becoming a tiring and laborious task for many people. Hence, text summarizers, which are softwares that, when given a particular text input, summarize it effectively and output only the important parts in the text, while discarding the unimportant or redundant bits. A number of Text summarizers currently exist on the internet, for free and for a cost as well. Many of them are efficiently able to extract the important information and text out of the given input. However most of them are a single type of text summariser titled “Extractive Text Summarisers”, which, while fairly accurate, is based on a model that merely extracts the important texts from the given input as it is, without specifically phrasing it in any other way, or without semantic understanding of the text itself. This leads to some inaccuracies in the summarized texts, such as some irrelevant words being put in the summarized output, even though they are unimportant, or some important sentences or phrases missing out on being in the output, as they have not been discussed enough in the input. That is why, the proposed system of Query based Summarizer not only works on queries, but is also aimed to be an Abstractive Text Summarizer, which will use semantic and syntactic understanding of the given input to summarize it and deliver a clear, concise and accurate output.

Speech is the most familiar and habitual way of communication used by most of us. Due to speech disabilities, many people find it difficult to properly voice their views and thus are at a disadvantage. The re- search tackles the issue of lack of speech from a speech impaired user by recognizing it with the use of ML models such as Gaussian Mixture Model - GMM and Convolutional Neural Network - CNN. With properly recorded and cleaned muscle activity from the facial muscles it is possi- ble to predict the words being uttered/whispered with a certain accuracy. The intended system will additionally also have a visual aid system which can provide better accuracy when used together with the facial muscle activity-based system. Neuromuscular signals from the speech articulat- ing muscles are recorded using Surface ElectroMyoGraphy (SEMG) sen- sors, which will be used to train the machine learning models. In this paper we have demonstrated various signals synthesized through the ElectroMyography system and how they can be classified using machine learning models such as Gaussian Mixture Model and Convolutional Neu- ral Network for the visual-based lip-reading system.