Challenges posed by non-standard neutrino interactions in the determination of δCP\delta_{CP} at DUNE

One of the primary objectives of Deep Underground Neutrino Experiment (DUNE) is to discover the leptonic CP violation and to identify it's source. In this context, we study the impact of non-standard neutrino interactions (NSIs) on observing the CP violation signal at DUNE. We explore the impact of various parameter degeneracies introduced by non-zero NSI and identify which of these can influence the CP violation sensitivity and CP precision of DUNE, by considering NSI both in data and in theory. In particular, we study how the CP sensitivity of DUNE is affected because of the intrinsic hierarchy degeneracy which occurs when the diagonal NSI parameter $\epsilon_{ee}=-1$ and $\delta_{CP}= \pm 90^{\circ}$

Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter

സാമ്പിൾ ശേഖരണം ശാസ്ത്രീയ മണ്ണുപരിശോധനയ്ക്ക്

സാമ്പിൾ ശേഖരണം ശാസ്ത്രീയ മണ്ണുപരിശോധനയ്ക്ക് Sample collection for scientific soil analysi

The seed and agricultural biotechnology industries in India: An analysis of industry structure, competition, and policy options

Since the late 1980s, technological advances and policy reforms have opened up new opportunities for growth in India's seed and agricultural biotechnology industries. The impacts of such changes have been significant in India's cotton sector, but less so for the country's main cereal crops, where both yield and output growth rates have been relatively stagnant. Some public policymakers and corporate decisionmakers are confident that the private sector will help reverse these trends, arguing that the right combination of new technological solutions and progressive policy reforms will unleash a significant increase in private investment in productivity-enhancing products and services. The structure of India's seed and agbiotech industries, as well as the policies designed to support their growth, will be a significant determinant of this expected impact. This paper examines the structure of India's cereal seed and agbiotech industries, its potential effects on innovation and social welfare, and the policies that may improve both industry performance and the delivery of new technologies to resource-poor, small-scale farmers in India's cereal production systems. We focus our analysis on indicators and scenarios within India's agricultural innovation market for improved seed and agricultural biotechnology products. This market includes firms engaged in the development, commercialization, and marketing of new seed-based technologies; it is characterized by a high level of knowledge intensity, relatively high levels of R&D investment, significant barriers to entry, significant levels of regulation, and relatively few products in the market. And it is within this market that factors such as strategic corporate behavior and public policy can affect the balance between a socially desirable rate of innovation, on the one hand, and a socially desirable distribution of the gains from innovation among consumers, farmers, and innovators, on the other hand.Seed markets, Agricultural biotechnology, industrial organization, Cereal crops,

Achieving Optimal Performance and Quality in LAN and WLAN for Mission-Critical Applications

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-981-99-6974-6_17Voice Over Internet Protocol (VoIP) properties are vital for its reliability in mission-critical applications. This research aims to find network topology, call signalling and voice codecs property combinations that meet reliability targets of VoIP communication in a Small Office Home Office (SOHO) environment where network resources may be limited but reliable and secured operation is essential. Local Area Network (LAN) and Wireless LAN (WLAN) scenarios are evaluated using Quality of Service (QoS) and Mean Opinion Score (MOS) measurements to find which property combinations satisfy predefined classes; best quality and best performance. The research extended Roslin et al. [1] on LAN VoIP to WLANs, and validated Khiat et al. [2] s and Guy [3]’s work that argued SIP was effective in optimal set up. This research found that VoIP combinations offer some desirable characteristics, but at the cost of other properties required, leading to categorisation being based on the interpretation of the results, concluding that though, not ideal for mission-critical applications, combinations function well in replicating real-world scenarios. The analysis also established VoIP's scalability for application-based configurations, impact of VoIP’s modularity and ease of configuration in achieving user expectations. Further property testing can solidify VoIP’s capabilities to function for mission-critical environments