Journal of Advances in Computer Engineering and Technology
Volume:6 Issue: 1, Winter 2020

Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local information to develop a robust face recognition system.In this papers it is proposed that hybridization of global and local facial features and combination of 2D and 3D modality helps in improving performance of face recognition system. The main issue of existing face recognition systems is the high false accept rate which is not desirable when security is the main concern. Most of the existing face recognition techniques overcome these problems with some constraints. However, the proposed methodology has achieved better results and handled all the three issues successfully. Also the use of 2.5D images (Depth Map) and dimensionality reduced data (Eigen faces) has shown that the system is computationally reasonable.

A Detailed Exploration of usability statistics and Application Rating on short-range Wireless protocols Bluetooth (IEEE 802.15.1), ZigBee (IEEE 802.15.4), Wi-Fi (IEEE 802.11) and NFC (ISO/IEC 14443) has been performed that being representing of those prominent wireless protocols evaluating their main characteristics and performances in terms of some metric such as co-existence, data rate, security, power consumption, joining time are analyzed and presented. Furthermore, considering the file sharing, tag connection, payment method apply and security parameters, usability statistics, application rating and research output is also depicted so that one can easily identify the scope of the protocols, and can visualize the most trending and demandable wireless protocol. A deeply analyzed bar graph illustrates the most demandable wireless protocol . This can be applied in any user's work in the Wireless Network lab and also be implemented in any real-world applications for the appropriate components and devices among the protocols in proper fields.

Accurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the intensity of the disease. The applied method first employed feature selection algorithms to extract features from images, and then followed by applying a median filter to reduce the dimensions of features. The brain MRI offers a valuable method to perform pre-and-post surgical evaluations, which are keys to define procedures and to verify their effects. The reduced dimension was submitted to a diagnosis algorithm. We retrospectively investigated a total of 19 treatment plans, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. The simulation results demonstrate that the proposed algorithm is promising.

Carbon Monoxide (CO) is the most abundant air pollutant gas and accumulates rapidly to dangerous concentrations even in areas that seem to be well ventilated. Carbon monoxide detectors/alarm systems exist but people who are old, hearing impaired, partially sighted or heavy sleepers may not get the warning or find it difficult to wake up and get out in the event of dangerous concentration of CO in their homes. This paper presents the development of a smart CO monitoring and control system to control the ventilation in a room when carbon monoxide concentration is at a level dangerous to human health. The system is comprised of a microcontroller interfaced with CO sensor (MQ-7) and ultrasonic distance sensor (HC-SR04) for CO concentration sampling and window state determination respectively. A third component interfaced with the controller is a DC motor, which accordingly control the window when the concentration of CO is high. A mechanism was provided to ensure that the fan in the room is ON and the window is completely open whenever CO concentration is high to ensure quick restoration of the air quality. Results from the performance evaluation of the system showed that it achieved an average response time of 6 seconds and consumed 321.62mW and 652.82mW of power during sampling and control respectively. The obtained results showed that the system is capable of responding quickly to dangerous concentration of CO, thus a desired attribute of CO monitoring systems hence, can adequately replace the existing systems with less power consumption.

This study presents the kinematic analysis of a four-degree freedom medical robotic arm using the Matlab and the robotic-tool, the arm was designed using a solid work program, As well as details of the control of the real design of this arm using Arduino Mega 2560, The specialist enters the position to be reached by the automatic arm (injection position), Or moving the arm to any position by entering the values of the corners of the joints, In this search, we have moved the arm to the selected position Without injecting into the muscle which need another study and a medical sensor determines the amount of needle entry in the muscle, According to criteria determined by the specialist and can be added to the designed interface.Key words: Kinetic study, medical robot, four degrees freedom, Labview, Arduino Mega 2560.Key words: Kinetic study, medical robot, four degrees freedom, Labview, Arduino Mega 2560.

In this paper we present Document analysis and classification system to segment and classify contents of Arabic document images. This system includes preprocessing, document segmentation, feature extraction and document classification. A document image is enhanced in the preprocessing by removing noise, binarization, and detecting and correcting image skew. In document segmentation, an algorithm is proposed to segment a document image into homogenous regions. In document classification, Neural Network (Multilayer Perceptron- Back propagation) classifier is applied to classify each region to text or non text based on a number of features extracted in feature extraction. These features are collected from different other researchers’ works. Experiments were conducted on 398 document images selected randomly from printed Arabic text database (PATDB) which was selected from various printing forms which are advertisements, book chapters, magazines, newspapers, letters and reports documents. As results, the proposed segmentation algorithm achieved only 0.814% as ratio of the overlapping areas of the merged zones to the total size of zones and 1.938% as the ratio of missed areas to total size of zones. The features, that show the best accuracy individually, are Background Vertical Run Length (RL) Mean, and Standard Deviation of foreground.