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In this paper a PID and ACO based wireless heating system is proposed. The heater node with temperature sensor senses the temperature of the room and compares it with set temperature for the system. The required temperature is set by RF remote control and the feedback signal generated by heater node generates the error signal to PID controller. The optimized value of KP, KI, KD are calculated by using ACO algorithm. The PID controller output data fed to the dimmer circuit to control the dimming level of heating system. In the proposed system upto 8.72% power can be saved in two hours with the experimental setup. [ABSTRACT FROM AUTHOR]

Abstract: Well patterned Ag nanoparticle (NP) arrays were fabricated on TiO2 thin film (TF). Scanning electron microscopy shows Ag NPs distribution on the TiO2 TF surface. The overall Raman scattering intensity increased for the Ag/TiO2 TF system. The enhanced Raman scattering of Ag/TiO2 TF was due to the formation of strong electromagnetic field around the Ag NPs under photon excitation. Optical absorption measurement shows three times enhancement in visible light absorption for the Ag NPs containing samples as compared to bare TiO2 TF. The photoluminescence emission intensity from the Ag NPs/TiO2 TF sample reduced due to the formation of Ag–O complex on the TiO2 surface. [Copyright &y& Elsevier]

The substantial increase in the human population dramatically strains food supplies. Farmers need healthy soil and natural minerals for traditional farming, and production takes a little longer time. The soil-free farming method known as vertical farming (VF) requires a small land and consumes a very small amount of water than conventional soil-dependent farming techniques. With modern technologies like hydroponics, aeroponics, and aquaponics, the notion of the VF appears to have a promising future in urban areas where farming land is very expensive and scarce. VF faces difficulty in the simultaneous monitoring of multiple indicators, nutrition advice, and plant diagnosis systems. However, these issues can be resolved by implementing current technical advancements like artificial intelligence (AI)-based control techniques such as machine learning (ML), deep learning (DL), the internet of things (IoT), image processing as well as computer vision. This article presents a thorough analysis of ML and IoT applications in VF system. The areas on which the attention is concentrated include disease detection, crop yield prediction, nutrition, and irrigation control management. In order to predict crop yield and crop diseases, the computer vision technique is investigated in view of the classification of distinct collections of crop images. This article also illustrates ML and IoT-based VF systems that can raise product quality and production over the long term. Assessment and evaluation of the knowledge-based VF system have also been outlined in the article with the potential outcomes, advantages, and limitations of ML and IoT in the VF system., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are used in this study to analyze and characterize Electrocardiogram (ECG) signals. This technique consists of three stages: ECG signal preprocessing, feature extraction, and ECG signal order. The 3D wavelet transform is a signal preprocessing technique, de-noising, along with wavelet coefficient extraction.•SVM is used to categorize the ECG through each of the nine heartbeat types recognized by the various classifiers. For this work, around 6400 ECG beats were looked at over the China Physiological Signal Challenge (CPSC) 2018 arrhythmia dataset.•The best degree of exactness was acquired when level 4 rough constants with Symlet-8 (Sym8) channel were utilized for arrangement. Utilizing the ECG signals from CPSC 2018 data set, the SVM classifier has a normal precision of 99.02%, which is much better than complex support vector machine (CSVM) 98.5%, and weighted support vector machine (WSVM) 99%.•The suggested approach is far superior to others in terms of accuracy, and classification of several diseases of arrhythmia., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V.)