Modeling of Schottky diode and optimal matching circuit design for low power RF energy harvesting.

This work designs and implements a single-stage rectifier-based RF energy harvesting device. This device integrates a receiving antenna and a rectifying circuit to convert ambient electromagnetic energy into useful DC power efficiently. The rectenna is carefully engineered with an optimal matching circuit, achieving high efficiency with a return loss of less than -10 dB. The design uses a practical model of the Schottky diode, where both RF and DC characteristics are derived through extensive experimental measurements. The results from both experiments and simulations confirm the effectiveness of the design, showing its proficiency in efficient RF energy harvesting under low-power conditions. The antenna produced operates in the wifi band with a gain close to 4 dBi and a bandwidth of 100 MHz. With a load resistance of 1600 Ω, the proposed device achieves an impressive RF-to-DC conversion efficiency of approximately 52% at a low incident power of -5 dBm. These findings highlight the potential and reliability of rectenna systems for practical and efficient RF energy harvesting applications. The study significantly contributes to our understanding of rectenna-based energy harvesting, providing valuable insights for future design considerations and applications in low-power RF systems.

New multi-injection commutation topology for circuit breakers of HVDC transmission lines.

Power-sharing between countries has an essential effect on increasing the power system's reliability and allowing a resilient energy market. High voltage direct current (HVDC) transmission systems are preferable for long-distance transmission to decrease power losses. However, HVDC transmission lines have many protection challenges including the differentiability between various types of HVDC circuit breakers (HVDC-CB). Although mechanical HVDC-CBs suffered from long response time, they superseded their solid-state counterparts in terms of price and power losses. In this paper, a multi-injection commutation system MICS-HVDC-CB is developed to provide economic and fast response HVDC-CB. The proposed breaker doesn't add external elements to avoid any price increase but instead modifies the existing topology. The MICS consists of multiple L-C commutation circuits inserted sequentially following the receiving of the tripping signal. The proposed MICS-HVDC-CB was tested upon a real transmission line using ATP simulation software. The results emphasize that the developed MICS-HVDC-CB decreased the arcing time to 38.5% and 20% compared to passive and active DC-CBs. The impact of cooling power, arcing time constant, and fault resistance was also investigated. The results showed the effectiveness of the proposed MICS topology in reducing the arcing time while keeping a simple and economic breaker structure.

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

Effective multi-sensor data fusion for chatter detection in milling process.

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles.

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.