Salam Kadhim Taher Al-Khammasi

Power consumption is considered one of the most significant challenges in the wireless network sensors (WSNs). In this paper, an investigation of the power consumption is done by making a comparison between static and dynamic WSNs. We have compared the results of the static network with the results of the dynamic network. Static and dynamic wireless Sensor networks have the same architecture (Homogenous) and proposed protocol. Depending on the suggested protocol, the simulation results show that the energy consumption in the static wireless sensor network was less than the dynamic wireless sensor network. However, moving the sensors in the dynamic WSN present real improvement in delivering packets to the base station. In the proposed routing protocol, transmitting data process is done in a hierarchal way. Cheap sensors are introduced and deploy them intensively to improve the QoS in the network. The final results and the conclusion are reported.

In this study, current network architecture and characteristics of the campus network at the University of Kufa (UoK) is presented and survey. The research methodology includes preliminary study, data collection and analysis, modeling, proposing and evaluation phase. A survey questionnaire is conducted to evaluate the current network performance from the user’s perspective and administrators for different colleges. Data are collected from 186 respondents that included 178 employees and 8 admins through online and paper surveys. Survey results have shown that most users are not satisfied with the existing network performance regarding several network services such as availability, Quality of Service (QoS). Additionally, the survey is validated by three experts through a structured interview. Most of the feedback from the experts match the results of the questionnaire. Thus, after identified several factors that could be influenced, we proposed a number of criteria to enhance the performance of the current network such as fault-tolerance, scalability, Quality of Service (QoS) and security. To achieve the proposed criteria, packet tracer software is used to design and simulate the new network architecture. The proposed campus network design is capable of delivering a service with 24/7 availability, ability to accommodate the increased number of users in future, the ability of packet prioritizing and improved security. Finally, a structured interview is conducted to validate the proposed design by five experts. Most of the feedback of the experts' match the results of the research that led the proposed switched campus network is acceptance.

This work represents the design and verification of three different finite impulse response (FIR) filter implementations for removing the noise of electrocardiogram (ECG) signals. Generally, ECG signals may be contaminated with different noise sources such as body movement and respiration, electromyography (EMG) interference, power line interference and the baseline wander noise. The FIR filter coefficients are calculated to attenuate the 60 Hz frequencies. The advanced filter design tool available with MATLAB is used to first determine the FIR filter coefficients. These coefficients are then used in three different FIR filter implementations: regular implementation, pipelined implementation and pipelined multiply-accumulate (MAC) implementation. The three implementations are designed using VHDL and the Quartus II design toolset. A test bench is also designed to verify the operation of each filter implementation, and the Modelsim simulator available with Quartus is used to run the tests. The synthesized reports for the three different implementations show the resource utilization and the maximum operating frequency. As a result, the regular (direct) design produces the simplest design but consumes more resources and operates at lower frequencies. The pipelined architecture consumes more resources but it enhances the operating frequency. The pipelined MAC implementation requires the least resources and operates at extremely higher performance, however, the main drawback is its complexity. The hardware implementations can be further viewed as an industrial ECG monitoring framework where system dynamics modeling can be applied to minimize the risks associated with the framework using any of the three implementations.

In this poster, we are designed and implemented an avoidance obstacle robot by using ROS (Robot operating system) as main platform. Neural Network algorithm has been used to program the robot. The algorithm has been written in Python programming language. In the hardware part, Arduino Uno Board with Ultra sonic sensor have been used to to detect the obstacles. Our contribution will be how to make the Robot detects the obstacle using neural network by learning himself from the environment and save that data which is getting by the ultra-sonic sensor to the base station so when it comes back to the same environment, the robot will not need to do the same procedure because the data already saved to the Base Station. All the related variables like Velocity, Acceleration and distance, etc. will get from ROS platform. The ROS will minimize the coding and gives us relative results. The communication between the robot and the base station will be wireless.