A phased array equipped with double phase shifters (DPS) can reduce interference and enable multi-target vital sign monitoring (VSM) based on a single receive antenna. The DPS-phased array can control both the magnitu...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
A phased array equipped with double phase shifters (DPS) can reduce interference and enable multi-target vital sign monitoring (VSM) based on a single receive antenna. The DPS-phased array can control both the magnitude and the phase of the signal transmitted by each antenna, allowing for flexibly creating a desired beampattern. We consider the DPS-phased array weight design problem, taking into account practical constraints on the phase shifters, such as deviations from the nominal phases and insertion loss. The weights of practical phase shifters are selected so that the resulting DPS-phased array performs as close as possible to an ideal beamformer and transmits maximum power in the desired direction. The proposed design’s effectiveness is demonstrated in experiments where the transmit power is focused on a specific human target to monitor corresponding vital signs. Simultaneously, an adjacent but unwanted human target is effectively nullified.
Underwater optical communication links, mult hop scattering may result in beam temporal spread characterized via the impulse response, which causes in interference and deteriorates system performance. Thence, an under...
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Tidal energy is an environmentally benign, practical, and cost-effective renewable energy source. The hydropower potential appropriate site for electrical power production accessible in Bangladesh's coastline regi...
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Agriculture is an important sector of Mizoram domicile as more than half of its population relies on Agriculture as principle source of income and sustenance. Some farmers rely on the knowledge acquire from their pare...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
Agriculture is an important sector of Mizoram domicile as more than half of its population relies on Agriculture as principle source of income and sustenance. Some farmers rely on the knowledge acquire from their parents through explicit explanation, and by observing and modelling their practices. But most farmer often struggle to understand the method and type of crops to cultivate for better crop yield. Even experienced farmer believed that using more fertilizer result in better crop yield in spite of that it damages the soil properties. To resolve this challenge, this paper presents a Crop Recommendation System using Machine Learning, tailored for Mizoram, enhancing Agriculture practices towards sustainable development. The Crop Recommendation System analyzes historical data, soil properties, weather pattern and crop performance to recommend the best crop for a specific region and its condition. The aim is to provide the Crop Recommendation System with information about the soil and the condition of the region. The study utilizes various Machine Learning Algorithms such as Random Forest, Decision Trees, Support Vector Machine and Logistic Regression to make optimal recommendation. The results indicate that Random Forest provides superior performance of 99% across all the evaluations metrics. Although many prevention measures need to be taken to avoid complications such as overfitting, etc. The overall results suggested that Random Forest achieved the best results as compared to all the other state of the art algorithms utilized with the same preprocessing steps. This approach enhances the crop and soil. After a long and often complicated process of farming method and selection of crop problem the Crop Recommendation System will aid Mizoram farmers to achieve better crops, yield and higher profit.
This paper proposes a parallel resonant soft switching auxiliary circuit that enables zero voltage turn-on and zero current turn-off of microelectromechanical switches. This provides a scalable soft switching method t...
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The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As tra...
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ISBN:
(数字)9798350376913
ISBN:
(纸本)9798350388473
The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p
The Internet of Things (IoT) refers to a smart network of digitally connected physical devices and systems, with applications ranging from predictive maintenance, optimization, enhanced efficiency in manufacturing and...
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ISBN:
(数字)9798350344134
ISBN:
(纸本)9798350344141
The Internet of Things (IoT) refers to a smart network of digitally connected physical devices and systems, with applications ranging from predictive maintenance, optimization, enhanced efficiency in manufacturing and critical infrastructure. Over the years the applications of IoT have been extended from domestic to Industrial Internet of Things (IIoT). The increased integration of IoT in real-world applications exposes the networks to cyber threats, e.g., Man in the Middle and ransomware attacks. Such attacks on IoT networks impact their operational integrity, data confidentiality, and overall system reliability, especially in IIoT networks. Therefore, the timely and accurate detection of cyber attacks can save these networks from unauthorized access, malware propagation, and denial of service attacks. In this paper, we present a cyber attack detection technique using Edge-IIoTset dataset for IIoT applications. The proposed detection approach is based on binary and multi-class classification of cyber attacks in IIoT networks, leveraging machine learning (i.e., logistic regression, decision tree) and deep learning (i.e., recurrent and convolutional neural networks) algorithms. The proposed classification techniques showcase the effectiveness of the applied algorithms achieving an average accuracy of 90 %.
During their studies, many students drop out of one specialty, then apply and enroll in another. The state subsidized several semesters of their studies, then again subsidized the student's studies in another spec...
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The recent improvement of powerful Large Language Models is the key to automatically produce satisfactory written and spoken language, in contrast to the constraints of conventional template-based solutions. However, ...
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The sensational outcomes of machine learning (ML) are witnessed. As the big success relies on continual training with massive data encompassing sensitive information, deep neural network (DNN) models easily leak priva...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
The sensational outcomes of machine learning (ML) are witnessed. As the big success relies on continual training with massive data encompassing sensitive information, deep neural network (DNN) models easily leak private information. For instance, large pre-trained language models contain a substantial volume of private information, which can be acquired by querying with appropriate prompts. This raises concerns regarding privacy in ML, and DNN models are evolving for privacy-preserving ML (PPML).
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