A novel and efficient session spanning biometric and password based three-factor authentication protocol for consumer USB mass storage devices

This paper proposes a key agreement scheme after secure authentication to prevent the unauthorized access of the data stored in a Universal Serial Bus (USB) Mass Storage Device (MSD). Due to the system architecture of this proposed scheme, authorized users can store their data in a secure encrypted form after performing authentication. The novelty of this work is that users can retrieve the encrypted data in not only the current session but also across different sessions, thus reducing the required communications overhead. This paper then analyses the security of the proposed protocol through a formal analysis to demonstrate that the information has been stored securely and is also protected offering strong resilience to relevant security attacks. The computational and communication costs of the proposed scheme is analyzed and compared to related works to show that the proposed scheme has an improved tradeoff for computational cost, communication cost and security

Potential and Advantages of Maize-Legume Intercropping System

Intercropping provides enough scope to include two or more crops simultaneously in same piece of land targeting higher productivity from unit area. Maize, a cereal crop of versatile use, as planted in wide rows offers the opportunity for adoption of intercropping. The intercropping system with maize and legume is beneficial in multifaceted aspects. The success of maize-legume intercropping system largely depends on choice of crops and their maturity, density, and time of planting. Advantage of maize-legume combination of intercropping system is pronounced in the form of higher yield and greater utilization of available resources, benefits in weeds, pests and disease management, fixation of biological nitrogen by legumes and transfer of N to associated maize, insurance against crop failure to small holders, and control of erosion by covering a large extent of ground area. Though maize-legume intercropping system exhibits limitations like less scope of farm mechanization, dependence on more human workforce, and chance of achieving less productivity from maize, the system implies more advantages for small holders in developing countries where human workforce is not a constraint. The chapter has focused on beneficial impacts of maize-legume intercropping system

Effect of the summer pearl millet-groundnut intercropping system on the growth, productivity and competitive ability of crops under south Odisha conditions

A millet-based intercropping system is common in dryland and rainfed conditions. Pearl millet (Pennisetum glaucum L.) exhibits wide adaptability to different agroclimatic conditions and seasons, making it suitable for an intercropping system. Groundnut (Arachis hypogea L.) is a leguminous oil-seed crop that can be cultivated as an intercrop in various cereals and millets to enhance productivity and resource efficiency. Based on these facts, the present study was conducted at the Research Farm of Centurion University of Technology and Management during the summer season of 2022 to assess the effect of the summer pearl millet + groundnut intercropping system on the growth, productivity, and competitive ability of crops under the conditions of south Odisha. The experiment consisted of nine treatments. In case of pearl millet, the highest plant height at harvest was achieved in pearl millet (30 cm × 10 cm) + groundnut (1:1) (186 cm), while the maximum plant height of groundnut at harvest was observed in pearl millet (45 cm × 10 cm) + groundnut (1:2) (70cm). Dry matter production at harvest and leaf area index (LAI) at 60 days after sowing (DAS) of pearl millet were highest in pearl millet sole (857 g m-2 and 2.19, respectively). The maximum dry matter production at harvest was found in groundnut sole. The highest yield of individual crops was observed in their pure stands, with 2677 kg ha-1 and 2633 kg ha-1 of pearl millet grain and groundnut pod, respectively. Among mixed stands, pearl millet (30 cm × 10 cm) + groundnut (1:1) and pearl millet (45 cm × 10cm) + groundnut (1:1) showed superior values of different competition functions, such as aggressivity, relative crowding coefficient, monetary advantage, land equivalent ratio, and area time equivalent ratio. The results concluded that pearl millet and groundnut could be intercropped with a 1:1 row proportion with pearl millet spacing of either 30 cm × 10 cm or 45 cm × 10 cm in south Odisha conditions

Real-time speech emotion analysis for smart home assistants

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this paper, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion