Telecommunication Engineering

Permanent URI for this collection

https://hdl.handle.net/20.500.11888/10120

Browse

Recent Submissions

Now showing 1 - 5 of 120
  • No Thumbnail Available
    Item
    ECG analysis system
    (2022) Aqraa Malak; Masri Tasbeeh
    The idea of our project based on the ECG analysis of the patient to detect some Changes that can cause certain diseases. This will enable us to detect these diseases as a result Of Specific changes in specific parts of the signal in the ECG signal, such as interval, Amplitude .as each of these changes is linked to a particular disease. We will collect the ECG data from the patient by placing ECG sensor on specific areas of the Patient’s body then we will transfer these data to the raspberry pi that attached to the ECG Sensor.Using the Matlab, we will analyze the signal to detect the QRS-waves
  • No Thumbnail Available
    Item
    Liver Disease Prediction Using Machine Learning Algorithms
    (2022) Hanani Worood; Awad Areej
    Nowadays technology takes a huge space in our lives especially in medical applications. The detection of liver disease at a preliminary stage is important for better treatment. It is a very challenging task for medical. This project will take the responsibility of decreasing the detection time to save patients life. This motion will implement using machine learning techniques such as SVM, KNN, CNN and ANN. It will compare between these algorithms and decide the best algorithm to build a complete system using python to detect the liver disease fast and efficiently. After the study CNN algorithm turned out to be the best algorithm to implement and provides more accuracy than other algorithms.
  • No Thumbnail Available
    Item
    The Effect of Power Increment on Cell Breathing and Capacity
    (2022) Qashou Ahmad
    Over the past decade, wireless communications have seen exponential growth and will certainly continue to witness spectacular developments due to the emergence of new interactive multimedia applications and highly integrated systems driven by the rapid growth in information services and microelectronic devices. However, In the 3G mobile networks, broadband data access at high transmission rates will be needed to provide users with packet-based connectivity to the services. In 3rd generation (3G) systems, although there is no need for frequency planning as in 2G systems, the limitation of both capacity and coverage due to interference levels in the system makes the planning task for 3G systems much more complex than for the 2G systems. In 2G systems, coverage prediction and capacity estimation are mostly separable. In traditional FDMA/TDMA systems, coverage is purely determined by RF transmission aspects, and the maximum capacity of the cell is only limited by the number of radio terminals at the base station (BS). In contrast, since the communication channels are separated by different time and frequency slots, in a CDMA 3G system, all users access the same frequency band simultaneously. Each communication channel is separated by modulating the data signal with a noise-like carrier, which is unique to the link, and spreading the modulated signal over a large frequency bandwidth. Since the signal appears like noise over the channel, the signals from all other users constitute a certain level of interference. This leads to a new definition of capacity, that of "soft capacity." According to this concept, it is permissible to accept more users into the cell at the price of a slight loss in quality in terms of mean signal-to-interference ratio (SIR). Due to the soft-capacity nature of CDMA networks, the coverage of a cell depends intensely on the desired QoS in terms of sustainable interference level, spatial mobile user distribution, and corresponding time-dependent user traffic intensity. Hence, on the dynamics of the power control procedure used to adjust interference. The fact that capacity in CDMA networks is influenced by the number of users in the cell has let the cell coverage area be considered elastic. This well-known cell breathing effect leads to an iterative approach to compute the power allocated to each user and the level of interference. These computations depend directly on the user locations and on the services (data rate) used. Thus, 3G-radio network planning is performed through simulations of user and service distributions. Cell breathing happens when part of the cell loses coverage and becomes a "dead area". This can be explained when a new mobile user is admitted to the network, the interference observed by each user in the cell increases, and their SIR at the BS decreases. As a result, users are required to transmit with higher power to compensate for the increase in the interference level. Eventually, as more users are admitted to the network, users at the edge of the cell are transmitting at their maximum power. As they move closer to the cell boundary, they will run out of the necessary power to maintain the connection; they will be dropped. In effect, cell coverage has shrunk. This means that during periods of low traffic, such as in the early morning, users can take advantage of a relatively large footprint for cell coverage. But as the traffic level rises during the day, users on the outer edges of a particular cell may effectively lose coverage and suddenly find themselves unable to make or receive calls. In this project, we aim to create a simulation model that shows the effect of increasing the transmitted power of a cell in a geographical coverage area during periods, of low traffic and when the traffic level rises during the day. Such model will be verified by collecting data through comprehensive drive test in the targeted cluster or region while monitoring the required cluster KPIs, which will determine the performance of cell throughout the day, therefore enables us to get a clearer understanding on the relation between the increasing the power and the traffic load on the cell's coverage area.
  • No Thumbnail Available
    Item
    Traffic Management System- Based IOV
    (2022) Shanaah Malik; Abu Zeneh Tamer; Darwazeh Sarah
    The project demonstrates the effectiveness of VANET as the future of vehicular networking. The project is composed of two parts - the first part shows the communication properties between VANET neighbors, such as average number of neighbors at different lane densities, the number of neighbors a car can maintain and for how long and the second part demonstrates V2I and V2V communications graphically. The simulations were developed using python 3 along with matplotlib, numpy and scipy modules. The objective of this simulation project developed in Python is to explore implementation of a simple epidemic-based routing mechanism designed to support reliable message dissemination in vehicular ad-hoc networks. In this project, the simulation can be carried out under different mobility by adopting different distributions of vehicle speed and inter-vehicle space, which provides several typical traffic scenarios for studying the epidemic routing mechanism.
  • No Thumbnail Available
    Item
    Women Safety Android Application
    (2021) Khalil Rahaf; Takrouri Renad; Zubidi Lama
    This project proposes an Android application called "Women Safety" build by Android Studio platform based on Global System for Mobile (GSM) & Global Positioning System (GPS). That can help them to stay safe via varied safety features like provide women the location of the nearest police center, the system sends SMS (short message service) and calls on one of the numbers feeded into the system with the location. This solution come with increase in people using smart phones, so can be used smart phones efficiently for personal security , reduce the risk and help to identifying the location of person who is in danger. The system process is briefly illustrated as follows: 1. First, create the app on the android studio program and install it in the Android smartphone. 2. Then, before opening the application, make sure that all the application permissions are activated, including GPS and also the internet access. 3. After that, create account for the new user then fill the numbers and email for the people you want to contact in case of emergency. 4. Now, the home page will open and we can see the current location for the user. 5. The app is ready to send a message alert and the current location to the people in the list, also we can take a picture and record video. 6. We can make a call to the people in the list and the police, also the location of the nearest police station will appear.