IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Fiber Bragg Grating sensors have a wide range of applications, ranging from their use for health monitoring, in medical applications and also as biomedical sensors, among others. Moreover, since fiber Bragg gratings have many advantages that qualify them to be of great benefit, of course, the most important of these applications are vital signs of human health condition Such as blood pressure, heart rate (pulse rate), and body temperature. The Vital-signs are noticeable variables. The temperature and blood pressure are changed according to the physical, involuntary, nervous and psychological state of the person. Therefore, the measurement of vital signs is very necessary, In this work, fiber Bragg grating sensor has been designed and simulated to study the performance of fiber Bragg grating sensor as a Body temperature of human beings ranged from (35°C_to 40°C, which is from hypothermia to hyperthermia) and blood pressure that ranged between lower and higher extremities (40 to 190 mmHg ) from hypotension to hypertension, using optigrating and optisystem simulation softwar. The designed sensor was very sensitive to human temperature and blood pressure ranges which were 13.632 pm/oC and 15.75 pm/mmHg, respectively.

Sentiment Analysis (SA) is a field of Natural Language Processing (NLP) whose goal is to extract the emotion, sentiment or more general opinion expressed in a human-written text. Opinions and emotions play a central role in human life. Therefore, there are many academic researches in this field for processing many languages like English However, there is scarce in its implementation with addressing Arabic Sentiment Analysis (ASA). It is a challenging field where Arabic language has a rich morphological structure and there are many other defies more than in other languages. For that, the proposed model tackles ASA by using a Deep Learning approach. In this work, one of word embedding methods, such as a first hidden layer for features extracting from the input dataset and Long Short-Term Memory (LSTM) as a deep neural network, has been used for training. The model combined with Softmax layer is applied to turn numeric outputs from LSTM layer into probabilities to classify the outputs to positive or negative. There are two datasets that are used for training the model separately with each one. The first one is ASTD dataset as a dialectal Arabic type about different tweets from internet, the results with this dataset is compared with another academic work that used the same one. The results from this work outperforms through accuracy about 14.95% and F-score about 15.14% more than what performed in the previous work. The second one is HTL dataset as a modern standard Arabic type about opinions of reviewers on different hotels from several countries. This dataset is bigger in size than the first one to show the size effect on the results of this model. So, the accuracy increased about 11% and F-score about 10.8% more than what performed with the first dataset.

The management of faults in Wireless Sensor Networks (WSN) has been considered recently. The problem of tolerating the detected fault is solved by presenting different methods from numerous researchers. Moreover, the software engineering approaches have been adopted to introduce methods with high reliability. In this paper, a fault tolerance method is proposed for WSN based on the software engineering self-checking process to deal with the faults that affect energy consumption in the network and make it drop earlier. The proposed method detects the appeared fault at any sensor node and recovers the faulted readings by computing the average value of its neighbor nodes. In addition, this process is continued until the faulty sensor is fixed by the maintenance team. The proposed method is tested over different case studies and the obtained results prove the claim of the paper's idea.

This paper presents an enhancement of the output performance of a linear buck converter system for the mobile (smartphone) devices using an adaptive digital Proportional–Integral-Derivative (PID) controller with off-line swarm optimization algorithm. The work focuses on improving the use of using single-input single-output (SISO) digital Field Programmable Gate Array (FPGA)-PID to control the linear buck converter system. The goal of the proposed adaptive SISO-FPGA-PID voltage-tracking controller is to rapidly and precisely identify the optimal voltage control action (optimal on-off duration time) that is used to control the buck converter output voltage level in order to avoid the troubleshooting hardware problem issues on mobile devices. The Particle Swarm Optimization (PSO) algorithms are used to find and tune the three weights of the SISO-FPGA-PID controller. The numerical simulation results and the experimental work using Spartan-3E xc3s500e-4fg320 board with Verilog hardware description language (HDL) show that the proposed controller is more accurate in terms of voltage error and the number of function evolutions are of high reduction. As well as to generate a smooth voltage control response without voltage oscillation in the output by investigating under mobile applications variations such as using Bluetooth, WI-FI, and CPU operating voltage when these results are compared with other controllers.

Wireless Sensor Networks (WSNs) can be the most important solution for several problems, particularly in emergency cases. Software engineering security for WSN can confirm four goals including confidentiality, integrity, authentication, and availability. In this paper, an authentication method for WSN is proposed based on lightweight authentication and key management protocol as well as concepts of software engineering. Moreover, the interleaving process is added to the adopted protocol to improve the security side. The proposed method uses a Kath hashing in addition to salt and hash: the MD5 algorithm. This is to provide an allowance for the authentication of the added node to join the network. The proposed method is tested over different case studies and the obtained results show the superior performance for it in terms of processing the added nodes.

Autonomous vehicle navigation has witnessed a huge revolutionary revision regarding development in Micro-Electro Mechanical System (MEMS) technology. Most recently, Strapdown Inertial Navigation System (SDINS) has successfully been integrated with Global Positioning System (GPS). However, different grades of MEMS inertial sensors are available and choosing the convenient grade is quite important. Noises in inertial sensor are mostly treated through de-noising the additive errors to improve the precision of SDINS output. Unfortunately, integration in SDINS mechanization causes a growing in SDINS error output which considered the main challenge in integrating MEMS inertial sensors with GPS. This paper aims to promote the long-term performance of the MEMS-SDINS/GPS integrated system. A new integrated structure is proposed to model the nonlinearities that exist in SDINS dynamics in addition to the error uncertainty in the inertial sensors' measurements. A robust Nonlinear AutoRegressive models with eXogenous inputs (NARX) based algorithm are designed for data fusion in the proposed GPS/INS integrated system. Validation for the proposed integrated system has been carried out using different field tests data in order to assess the accuracy of the system during GPS denied environment. The results obtained demonstrate that the proposed NARX model is applicative and satisfactory which shows a desired prediction performance.

We propose and analyse a silicon based hybrid modulator on the nano thin film of the lithium niobate or commonly known as silicon-on-insulator technology. The Mach-Zehnder stripe optical waveguide of electro-optical modulator operats at GHz frequencies with large bandwidth and low losses between electrical and optical frequencies.The design and simulation of Mach-Zehnder modulator is based on a hybrid integration platform of silicon and lithium niobate that satisfies a single mode condition. The Silicon stripe waveguide is of 0.6 µm thickness in a silicon on insulator (SOI) of width 15 um and 0.05 um thickness x-cut LiNbO3 thin film, all sets use the pulse laser deposition (PLD) method. The Optical electric field distributions and effective mode area in the optical-waveguides were studied and discussed in this designated waveguide.The relationship between the width of waveguides regions with effective mode index and effective mode area was investigated. At 0.6 um width of waveguide and 0.2 um thickness, the effective mode index 1.9802 was recorded while the effective mode area 0.144 um2 was monitored. This shows the decrement in both: the width and thickness of the waveguide with the effective mode index and effective mode area.