A Real Time IoT-AI Framework for Smart Agricultural Decision Making
Design of Reversible Full Adder or Subtractor Based on EPOE Expressions for Computational Applications
Automatic Room Light and Fan Controller with Visitor Counter using Arduino Uno
Denoising and Enhancement of Low-Light Images Using Adaptive Gamma Correction and Guided Filtering
IoT Network Intrusion Detection using Self Learning Evaluated Bird Swarm Optimization and a Deep Belief Network
Elastic Optical Networks: A Comprehensive Review on Emerging Technologies
The Impact of Substrate Doping Concentration on Electrical Characteristics of 45nm Nmos Device
Method of 2.5 V RGMII Interface I/O Duty Cycle and Delay Skew Enhancement
A Study on Globally Asynchronous and locally Synchronous System
Performance Analysis of Modified Source Junctionless Fully Depleted Silicon-on-Insulator MOSFET
Automatic Accident Detection and Tracking of Vehicles by Using MEMS
Efficient Image Compression Algorithms Using Evolved Wavelets
Computer Modeling and Simulation of Ultrasonic Signal Processing and Measurements
Effect of Nano-Coatings on Waste-to-Energy (WTE) plant : A Review
ANFIS Controlled Solar Pumping System
A Real Time IoT-AI Framework for Smart Agricultural Decision Making
Agriculture is being revolutionized by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI). Traditional farming techniques frequently rely on delayed observations and manual decisions, resulting in inefficient resource management and suboptimal yields. This study introduces an innovative IoT-AI system framework designed for real-time decision-making in agriculture. By combining IoT-based sensor data collection with AI models deployed at the edge, the framework provides immediate, actionable insights for improved irrigation, fertilization, and pest management. The system prioritizes low latency, scalability, and cost-effectiveness to support both small-scale and commercial farms. Initial tests demonstrate notable improvements in resource efficiency, prediction accuracy, and system responsiveness.
Reversibility plays a crucial role in achieving energy-efficient computations.This study proposes the design of low-cost self-control serial adder system is proposed, utilizing EPOE expression for the efficient and streamlined design of sequential circuits. This approach aims in minimizing the fixed inputs in sequential logic circuits and reduce quantum cost, common terms between outputs are optimally shared. Additionally, limiting the use of Tofoli gates with more than three inputs in the final circuit implementation helps lower the quantum cost of the resulting circuit.
This paper is about enhancing the use of resources in developed as well as developing countries. In today's technologically advanced world, this work prefers tasks to be completed automatically and without human intervention. Additionally, this study reduces the need for workers. Resource conservation is also very beneficial. With the increasing standard of living, the demand for automatic appliances has intensified, highlighting the need for designing advanced circuits that can optimize functionality and simplify daily activities. This current work can also be used to monitor the number of people entering or leaving a room, making it useful for preventing overcrowding and ensuring efficient space management. The "Automatic room light and fan controller with visitor counter using Arduino Uno" is a dependable circuit that takes over the responsibility of precisely counting the number of people or visitors in the room in addition to controlling the lights and fans. This circuit operates based on the number of people entering the room.
Image enhancement plays a crucial role in improving the visual quality of images for various applications, including medical imaging, surveillance, and computer vision. This work proposes a novel image enhancement method based on guided filtering and adaptive gamma correction to address the limitations of conventional approaches. The existing method relies on bilateral filtering and fixed gamma correction, which may not effectively preserve fine details and contrast in complex scenes. The proposed method replaces bilateral filtering with guided filtering for improved edge preservation and introduces adaptive gamma correction to dynamically enhance image contrast. The experimental results demonstrate that the proposed method achieves a significant reduction in Mean Squared Error (MSE) and an increase in Peak Signal-to-Noise Ratio (PSNR) across multiple test images. The results confirm the effectiveness of the proposed approach in achieving better visual quality while maintaining structural similarity.
Intrusion detection in IoT networks remains a challenging task due to the increasing complexity of cyber threats. This paper introduces a self-learning enhanced bird swarm optimization-deep belief network (EBSO-DBN) model to enhance the efficiency and accuracy of network intrusion detection. By incorporating an adaptive self-learning mechanism into EBSO, the proposed method dynamically adjusts the DBN parameters for improved classification performance. The comparative evaluation demonstrates that the proposed approach achieves a higher accuracy of 99.16%, precision of 99.51%, recall of 98.93%, and a significantly reduced false alarm rate (FAR) of 0.81%, outperforming the existing method, which achieved 97.72% accuracy, 98.67% precision, 97.04% recall, and a FAR of 2.22%. These results indicate that the proposed method effectively enhances intrusion detection, reduces false positives, and ensures a higher detection rate, making it a reliable solution for IoT network security.
Fixed grid is incapable of delivering and satisfying clients demands, as there is a massive increase in bandwidth demanded. To surmount this constraint, the terminology elastic optical network (EON) has been coined. Utilization and management of spectrum resources can be done effectively through EONs. EONs and their features are discussed, including super channel, the restoration squeeze scheme, and sliceable bandwidth variable transponders. The discussion also covered various modulation formats and transmissions like Nyquist wavelength division multiplexing (NWDM), orthogonal frequency division multiplexing (OFDM), and time frequency packing (TFP), followed by sliceable bandwidth variable transponder (SBVT) architectures.
