Al-Iraqia Journal for Scientific Engineering Research

A framework known as "smart healthcare" makes use of technologies like wearable's, and the Internet of Medical Things (IoT), In order to intelligently manage and respond to the needs of the health environment, we need to combine data, machine learning algorithms, wireless communication technology, and the Internet of Things (IoT) to link people, resources, and organizations; make health records easily accessible; and share the data widely. Medical sensors, or IoT, are one of the key components of smart healthcare. Due to the complexity of illnesses, Disease diagnosis often necessitates the use of multiple types of medical signals. The most crucial concern when employing multimodal signals is how to fuse them, which is a topic of growing interest among researchers. The newest generation of personal portable gadgets for telemedicine practice is wearable health monitoring systems. However, due to the inapplicability of the building process used for standard semiconductor equipment, wearable healthcare equipment research and commercialization are currently progressing at a very sluggish rate. Despite these obstacles, developments in materials flag, chemical analysis techniques, apparatus, and manufacturing processes created a groundwork for whole new wearable technology, which has continued to evolve.

Fast technological development has streamlined daily living. On the other hand, as a result of a shortage of emergency services, the growth of technology has also increased the incidence of traffic accidents, which cause enormous loss of life and property. Systems for monitoring vehicles and detecting accidents are highly dependable and secure, and they are especially useful for remotely monitoring vehicle activity. In this paper, based on the Global Positioning System (GPS) and the Global System for Mobile (GSM) Communication has been implementing, an effective system for monitoring vehicle movement and identifying any accidents that occur has been established GSM. The suggested method successfully made use of widely available hardware elements that integrate a smartphone and a vehicle unit that is mounted within the car. The proposed system would use General Packet Radio Service (GPRS) to automatically communicate and store vehicle data to the Internet of Things (IoT) every 10 seconds. In addition, the system will create an SMS Short Message Service (SMS) and send it to the user's smartphone in response to an SMS request received with the vehicle's current position information. At the user side, it has been an android smartphone for software application connected to the Thinger.io platform that has been used to view up-to-date car information. Only the authorized user who has access to the Thinger.io platform and the login and password may access this application due to its security. The suggested system's accuracy has been evaluated. The findings show that the suggested method can follow the vehicle high accurately

In recent years various modern improvements have been used to raise the performance of heat systems in various engineering and industrial applications. One of these improvements is the use of porous material. This research focuses on conducting a theoretical study (numerical simulation) via forced convection heat transfer of three channels of various cross-sections (square, rectangular, and triangular) with an equal hydraulic diameter of (0.15 m) with glass spheres filled of a diameter (0.012 m) as a porous martial. The lower surface of the test section is subjected to a uniform heat flux along the fluid flow of (5000 W/m2), while the upper surface is thermally insulated. The type of model used in this study (k- epsilon turbulent model) to simulate and analyze fluid flow inside three channels. This study aims to enhance the thermal properties of the fluid (water) and study its effect on the distribution of temperature, velocity, and pressure, respectively. The results showed that the channel with a triangular cross-section gives the highest distribution of temperature and pressure compared to the rest, and therefore it is considered the optimal design for the channels.

This paper produces an efficient, low-cost solution for communication between Infrastructure and vehicles by exploiting the passive UHF RFID features to deploy a scalable platform that overcomes the limitation of current technology GPS, WIFI and camera used in intelligent transportation system (ITS). The limitations are GPS inaccuracy, camera failure in bad weather, WIFI extra time handshake, and high deployment cost. The suggested system provides fast communication in real time that's required in warning message transmission between Infrastructure and vehicles, like speed limit warning and curve warning that the driver always misses or ignores. The proposed system contains a UHF RFID reader with a built-in antenna installed in the front pamper of the car connected to a microcontroller Arduino UNO by serial interface and a passive RFID tag deployed inside the road surface in the same line of the intended warning signal and this tag store the road information related to the warning signal. When the go over the RFID tag, it will retrieve its information, and a warning message will display on an LCD screen. This paper simulates all possible weather conditions and tries the system under four communication environments: water, dust, asphalt and free Air and compares the result accordingly.

Deep learning (DL) has recently shown great promise in supporting knowledge of electroencephalographic (EEG) as a result of its ability to discover visual features (feature representation) from original (raw) data. This review will look at the latest developments in the research area of the EEG by analyzing a largest amount of the recent and definitive publications on EEG based on DL for biometrics identification. It covers the latest developments in different parts of the DL-EEG methodology and offers valuable information about them in order to improve its implementation. Also, it will provide interested researchers with a brief overview of the prospects of applying DL typical EEG processing methods. In addition to highlighting interesting methods and trends that used to acquisition and analyses brain signals, the stimulations, feature extractions and classifications. We summarize our review in some recommendations and proposals in the hope of promoting effective viable research in this field. We have highlighted interesting approaches and directions from this extensive research in order to provide ample information for future research. This review revealed that the duration of time spent trying to collect EEG data ranged from (10) minutes or less to a long time of hours, and interestingly, we found that more than 50 percent of the research design their models using publicly available datasets. Furthermore, There about half of the researchers used unprocessed or preprocessed EEG samples to train their models. Compared to traditional approaches, DL had an improvement in accuracy of 4% among the most applicable studies. More importantly, we discovered that the majority of previous studies have poor reproducibility: it is extremely difficult or impossible to replicate the majority of research due to a lack of data and codes. The importance of the paper lies in helping the research community to share and develop work more effectively. And We'll also offer a list of suggestions for more studies in the future.

Fire is one of the most dangerous types of accidents that can occur and may cause huge harm to people's lives and threaten environmental resources by spreading quickly, which makes it out of control. This critical aspect requires an intelligent system to control it correctly and as soon as possible. To achieve this, a platform has been proposed for a fully automated fire detection system to overcome fire spreading, this goal has been achieved by utilizing the UAVs (Unmanned Aerial Vehicles) as an IoT service role equipped with Pixhawk4 as a flight controller and an onboard fire detection system using Raspberry-Pi4 equipped with a camera to detect the fires using Convolutional Neural Network (CNN) algorithm. The drone systems' particular properties of high speed, flexible movement, and easy control, as well as the embedded sensors in flight controller like gyroscope, accelerometer, and compass, make it the best choice for the real-time monitoring system. The whole system was tested and the real-time fire video was detected using the MobileNetv2 as a CNN pre-trained model which has trained and achieved an accuracy 99.79% .

Brushless DC motors (BLDC)are extensively used for high-control applications because of their small size, high efficiency, and high-performance qualities. It gained popularity since the permanent magnetic materials were improved, so the drive system must have precise positioning and smooth electromagnetic torque for high performance and precision. Numerous elements contribute to torque ripples in this motor; one of them is the type of permanent magnet material. In this paper, a 3-phase, four-pole, 1500 W BLDC motor was modeled based on RMxprt and Maxwell 2D software. Four magnet materials: alnico 5, ceramic 5, SmCo 28, and NdFe 35, were studied to show the effect of each magnet on motor performance. The finite element analysis (FEA) results showed that the motor's average torque and cogging torque are directly proportional to the magnet strength. Also, the torque ripple exists with using a lower magnet strength magnet. Ndfe35 and SmCo28 have strong mechanical properties, are relatively expensive, and have higher cogging torque and torque ripple. The other materials, Alnico 5 and ceramic 5, are less costly and have less cogging torque and torque ripple. The full load simulations demonstrate that weaker magnets can also provide lower torque. Due to the results presented, while choosing the magnet materials, the trade-off between nominal torque, torque ripple, and price should be taken into account. The methodology adopted in this research will allow motor designers to select the best magnet material,and leading to excellent motor performance without the requirement to construct and test several prototype motors.

Image security has attracted increasing interest, wherein image encryption is an effective and direct method that can be implemented using digital, optical, or hybrid techniques. The challenge is how to design a high-speed, and high-security level multiple color image encryption scheme which makes use of advanced techniques such as chaotic system and deep learning. This issue is addressed in this paper where a nine-dimensional (9D) chaotic-based Hybrid Digital/Optical Encryption (HDOE) scheme is proposed for triple color images. The scheme consists of cascading digital and optical encryption parts controlled separately by the chaotic sequences. The nine chaotic sequences are grouped into three sets, and each set is responsible for the encryption of one of the RGB channels independently. The digital part uses fusion, XOR operation, and scrambling. The optical part uses two independent chaotic phase masks in the optical Fourier transforms domain. A Denoising Convolution Neural Network (DnCNN) is designed to assist the robustness of the decrypted images against Gaussian noise. The simulation results reveal that the proposed triple-image HDOE scheme offers entropy of 7.9991, 7.9987, and 7.9991 bits for R, G, and B channels, respectively, and infinite Peak Signal-to-Noise Ratio (PSNR) for the decrypted images.

Using scientific knowledge, weather forecasters can predict what the atmosphere will be like in a particular place. It predicts snow, cloud cover, rain, temperature and wind speed. Since weather predictions consist of multidimensional and nonlinear data, they are one of the world's most challenging problems. Various machine learning algorithms and methods have been used in data mining for weather prediction, including Support Vector Machines, supervised and unsupervised machine learning algorithms, artificial neural networks, FPGrowth Algorithms, Hadoop with Map Reduce, K-medoids, and Naive Bayes. This survey briefly explains the methods used to build weather forecasting models to assist researchers in choosing the appropriate method for their model.