The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive ex

A solid Phase Extraction (SPE) followed by HPLC-UV method is described for the simultaneous quantitative determination of nine priority pollutant phenols : Phenol, 2- and 4-Nitrophenol, 2,4-Dimethylphenol, 2-, 2,4-Di-, 2,4,6-Tri-, and Penta- chlorophenol, 4 Chloro-3-methylphenol. The phenols were separated using a C-18 column with UV detector at wave length of 280nm. The Flow of mobile phase was isocratic consisted of 50:50 Acetonitrile: phosphate buffer pH=7.1, column temperature 45 C°, Flow Rate 0.7 ml/min. Calibration curves were linear (R2 = 0.9961-0.9995). The RSDs (1.301-5.805)%, LOD(39.1- 412.4) µg/L, LOQ(118.5-1250.8) µg/L, the Robustness (1.55-4.89), Ruggedness (2.82-4.00), Repeatability (2.1-4.95), Recoveries%

In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report

A load-shedding controller suitable for small to medium size loads is designed and implemented based on preprogrammed priorities and power consumption for individual loads. The main controller decides if a particular load can be switched ON or not according to the amount of available power generation, load consumption and loads priorities. When themaximum allowed power consumption is reached and the user want to deliver power to additional load, the controller will decide if this particular load should be denied receiving power if its priority is low. Otherwise, it can be granted to receive power if its priority is high and in this case lower priority loads are automatically switched OFF in order not to overload the power generation. The

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

Human identification is crucial in forensics for the investigation of large-scale disasters such as fires, epidemics, earthquakes, and tsunamis. Even though biometric identification using panoramic dental radiography (PDR) has been the subject of several studies in the literature, further study remains a necessary and challenging issue. In this research, a human identification system was developed based on a convolutional neural network (CNN) and contour transform (CT). The proposed system was implemented on a total of 1540 PDR from 302 individuals. The preprocessing applied to PDRs for enhancing and taking the Region of Interest (ROI). The features were extracted using CT transform. These features were fused with features extracted

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D