Iraqi Journal of Information and communication technology

Filter Bank Multi-Carrier (FBMC) modulation is one of the most significant enablers for future 5Gtechnologies. It is a modulation technique for resolving inter-carrier and inter-symbol interference using two possible methods: Frequency Spreading (FS) and Poly Phase (PP) implementation. Cyclic prefixes are used in OFDM for signal robustness, but they have some disadvantages in orthogonal frequency division multiplexing. FBMC is used to solve the disadvantages of OFDM and save bandwidth. In this paper, performance comparisons in terms of Bit error rate between OFDM and FBMC systems in AWGN as well as 5G channel which is SUI-6 multipath fading channel at the different number of subcarriers are presented. The obtained results show that FBMC over-performs OFDM in the SUI-6 channel and the improvement margin reaches 20%. Also, the results show that the improvement margin is increased as the number of subcarriers decreased

One of the main problems in robotics is the Inverse Kinematics (IK) problem. In this paper, three optimization algorithms are proposed to solve the IK of Humanoid Robotic Arms (HRAs). A Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), and Black Hole Optimization (BHO) algorithms are proposed to optimize the parameters of the proposed IK. Each optimization method is applied on both arms to find the desired positions and required angles with a minimum error. Denavit-Hartenberg (D-H) method is used to design the model of HRAs for both arms in which each arm has five Degree Of Freedom (DOF). The HRAs model is tested for performance by several positions to be reached by both arms at the same time to find which optimization algorithm is better. Optimal solutions obtained by SSO, PSO and BHO algorithms are evaluated and listed in a comparison table between them. These optimization algorithms are assessed by calculating the Computational Time (CT) and Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Calculation and simulation results showed that BHO is better than PSO and SSO algorithms from point of view of CT and RMSE. The worst RMSE is 0.0864 was calculated using the PSO algorithm. But longer CT is 7.6521 seconds, which was calculated using SSO. While the best RMSE and shorter CT.are2×10−7and 3.0156 seconds respectively were calculated by BHO. Moreover, in this paper, the Graphical User Interface (GUI) is designed for motional characteristics of the HRAs model in the Forward Kinematics (FK) and IK

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research blind algorithm has been adopted. For single image super-resolution, a deep learning method with super-resolution has been proposed. This approach can learn an end-to-end mapping between low-resolution images and high-resolution images directly. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error. Comparing with the traditional bicubic method in super-resolution, our proposed system (FSRCNN) has better performance in de-blurring with fast training speed and good results according to what the results showed for the metrics used to measure the performance of our system. The paper’s contribution is restoring blurred images corrupted by a known or unknown degradation function

Malicious software (malware) has long been used for illegal purposes; however, the number and variety of malware has increased dramatically in recent years, highlighting the need to enhance automated malware detection and classification. Neural network methodology has now progressed to the point that it might be able to out perform previous machine learning approaches such as Hidden Markov Models and Support Vector Machines (SVM). Asa result, convolutional neural networks (CNNs) have outperformed conventional learning methods in a variety of tasks, including image recognition. Motivated by this success, we propose a CNN-based architecture for malware classification. In this paper, we propose a malware classification and detection framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets, we applied a deep Convolutional Neural Network (CNN) based on the Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less overfitting problems than the current popular CNN models such as VGG16, The experimental results on a Malimg Dataset which is comprising 9,821 samples from 26 different families, Malware samples are represented as byte plot grayscale images and a deep neural network is trained to freeze the convolutional layers of Xception model adapting the last layer to malware family classification, The performance of our approach was compared with other methods including KNN,SVM, VGG16, etc. , the Xception model can effectively be used to classify and detect malware families and achieve the highest validation accuracy than all other approaches including VGG16 model which are using image-based malware, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion’s solution.

DHCP (Dynamic Host Configuration Protocol) is an important aspect in small and large networks since it facilitates the IP configuration of computers. However, DHCP has a lot of security concerns, the most serious of which being MAC address spoofing. Furthermore, current research is still unable to guarantee a suitable level of pro-tection; therefore, DHCP is vulnerable to different attacks, thus the essential objective of this paper is to propose solutions against DHCP attacks. This paper provides a deep clarification of how DHCP can be vulnerable and presents an explanation about how DHCP works and lists a summary about DHCP attacks, how the attacks occur and affect the security of the network, which threatens the network’s security. Main effective counter measures were proposed and implemented against DHCP attacks, resulting in a successful hindrance of the attacks. Some of the countermeasures decrease the chance of attack to be implemented. The paper provides recent methods to hinder DHCP attacks and provides a complete solution to mitigate DHCP vulnerability.

The Internet Engineering Task Force (IETF) standardized several protocols such as Constraint Ap-plication Protocol (CoAP) to run over WSN-IoT constrained devices. IPv6 is used to transmit packets over anIEEE802.15.4 radio link called 6LoWPAN. The routing protocol for low power and lossy (RPL) network enable connectivity of WSN over IoT. Nodes in RPL optimized their path using the objective function (OF), which depends on different node/link metrics. In this paper, the performance of the IoT-WSN stack consisting of CoAP, 6LoWPANand two proposed protocols based on RPL are evaluated. The two proposed protocols depend on a new OF based on link reliability and energy metric. The first is a modification of a recently introduced protocol called Energy Threshold RPL (ETRPL) protocol. ETRPL uses the remaining energy of the preferred parent node as a metric. The other protocol used a new metric that combines energy consumption with ETX for all paths to the root called EERPL. The protocols are tested with a full IoT-WSN stack and implemented using the Cooja simulator. The results showed that ETRPL and EERPL performed better than standard RPL in terms of energy consumption, average time delay, packet reception ratio, throughput and the number of dead nodes.

Wireless sensor network (WSN) require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. In this paper, cluster head (CH) selection is done using fuzzy logic approach and propose zone routing protocol (ZRP) for mobile WSN clustering scheme named as (ZRP-FCM)protocol which proposed fuzzy logic approach based on three factors of nodes for the selection as CH nodes such as, residual energy, the concentration, and the centrality of the node. also, the Ant-lion optimization (ALO) algorithm is applied in this work to determine the best position of mobility base station (BS) for gathering data, so-known as (ZRP-FMC-ALO) protocol. The performance of the proposed protocol is compared with the famous protocol such as the low energy adaptive clustering hierarchy protocol (LEACH) protocol. Simulations were carried out in MATLAB. The results show that the ZRP-FMC-ALO outperforms ZRP-FMC and LEACH in terms of networks lifetime (FNDs) by 16.19%, 304.5% respectively, (HND) by 14.2%, 184.29% respectively, and in terms of average consumed the ZRP-FMC-ALO outperforms LEACH after 1000 round by 84.9% and outperforms ZRP-FMC after3000 round by 30.7%) respectively

In mobile communication systems bit-rate reductions while maintaining an acceptable voice quality is necessary to achieve efficiency in channel bandwidth utilization and users satisfaction. As Long-Term Evolution(LTE) converging towards all-IP solutions and supporting VOIP service, the voice signals are converted into coded digital bit-stream and sent over the network. This paper proposes the implementation of a codebook excited linear prediction (CELP) voice codec algorithm based on two source rates of low 9.6Kbps and medium 16Kbps for achieving a perceptible level of voice quality, while efficiently using available bandwidth during the transmission over advanced LTE. The architecture of the proposed CELP codec model is implemented to decompose the voice signal into a set of parameters that characterize each particular frame at the encoder part, these parameters are quantized and encoded for transmission to the decoder. The investigation showed that the configuration of the link and the applied CELP codec mode mainly influence the obtained voice capacity and quality. For voice quality evaluation, the examination showed better voice quality obtained with a low value of noise weighting factor and for a higher codec rate. The quantifying also shows that the voice quality can be traded for the enhanced capacity since the low rate codec of 9.6kbps provide better voice capacity at the cost of inferior voice quality compared to the high rate codec of 16kbps. Also, this paper is achieved increasing in the capacity gain to a saturated value during the configuration of the system with a higher channel quality indicator (CQI) index. For transmit diversity(TD), about 500 and up to 1000 users per cell over 5MHz and 10MHz bandwidth respectively are obtained. For Open-Loop Spatial Multiplexing (OLSM) up to 1000 and 2000 users per cell over are obtained over 5MHz and10MHz channel bandwidth respectively

Content-Based Image Retrieval (CBIR) is a process for searching an image according to the content or feature within the image. Nowadays, most image retrieval applications have been developed to meet these needs, so this application will provide comfort in introducing and searching for an image. In this paper, we proposed an automatic image retrieval method that significantly impacts the images. Our proposed method outperforms current CBIR systems. The obtained results of our query image by using CBIR method have been compared with all the database images using Euclidean distance. The system was performed with ten different classes of 1000 images using a coral database. From our results, we find that the best results were obtained when we used higher decomposition levels with improving the performance of precision. Also, we conclude that the best results have been obtained when we used a median filter with accuracy reached 99 %.

Deception detection is becoming an interesting field in different areas related to security, criminal investigation, law enforcement and terrorism detection. Recently non-verbal features have become essential features for the deception detection process. One of the most important kinds of these features is facial expression. The importance of these expressions come from the idea that the Human face contains different expressions each of which is directly related to a certain state. In this research paper, facial expressions’ data are collected for 102participants (25 women and 77 men) as video clips. There are 504 clips for lie response and 384 for truth response(total 888 video clips). Facial expressions in a form of Action Units (AUs) are extracted for each frame with the video clip. The AUs are encoded based on Facial Action Coding System (FACS) which are 18 AUs. These are: AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 28 and 45. Based on the collected data, only six AUs are the most effective and have a direct impact on the discrimination process between liar and truth-teller. These AUs are AU 6, 7, 10, 12, 14 and 28, as discussed in detail in Table I.

The Direct sequence spread spectrum(DSSS) systems are one of the solutions for reliable and secured communications. Also, it is one of the approaches used by signals for transmitting bandwidth larger compared to the satisfied frequency related to the original information. The communication systems of Spread Spectrum were vital to suppress interference, complicating the detection and processing of secure communications, the technology of spread spectrum (DSSS) has been initially created for military applications. pseudo-noise PN code is considered the primary key that should be the same length and type in both the transmitter and the receiver. in the previous works, the PN code must be known by the receiver for retrieving the original data signal. In this work, it is considered that the transmitter sends data bits and wants to protect the sent data by making each bit send with variable PN code consisting of 127 bits randomly without knowing the receiver of that. Here the artificial neural network (ANN) is used as a tool in the receiver to find the correct PN code for each initial value of 7 flip-flops. so, the receiver could detect the transmitted data with BER = 0.

The main challenge of multimedia applications is how to transmission the Ultra-High Definition (UHD)video streaming in real-time over the internet. Real-time video streaming suffers from difficulties to be the flexible and efficient cause of the wide variation of the available internet bandwidth. To avoid the problems that introduce with the internet, in this work the HEVC with required video network adaptive streaming are proposed and tested using different six levels of three UHD video (4K, FHD, 720p, 4CIF, CIF and QCIF). Different experiments are applied to find the optimal configuration of H.265 encoding features for six levels to obtain the required PSNR with a range of 32-38 dB. The important part of this paper is a controller that worked incorporate with the encoder (H.265) to obtain the video streaming adaptation on the available bandwidth of the channel. The controller continuously reads the status of the channel buffer, then choosing the proper level of video to be transmitted over the channel. The work architecture is content two parts: First, the H.265 codec that applies on the three raw videos with optimal parameters configuration to compress them and get videos with lower bit rate and acceptable quality. Second, the compressed videos, based on controller selection, will be transmitted over two types of networks: Unicast and Multicast, and monitoring these two networks to evaluate their performance by obtaining different maps to choose the good one based on their effects on all levels of videos that are tested in this work.

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problems. Spectrum sensing is the main step of CR. Traditional sensing techniques sense one channel at a time while sensing the wideband spectrum which is required in 5G communications produces more challenges. Compressive sensing (CS) is a technology used as a spectrum sensing technique in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage measurement (sensing) matrix are required, and it plays an important role in the performance of CS. Selecting the best sensing matrix leads to low reconstruction error with high compression. The design of an efficient sensing matrix requires achieving low mutual coherence or satisfying the Restricted Isometry Property (RIP) with high probability. In the decoding stage, the recovery algorithm is applied to reconstruct a sparse signal from a few measurements. In this paper a new chaotic matrix is proposed based on the Chebyshev map and modified gram Schmidt (MGS). The CS-based proposed matrix is applied for sensing real TV signals as a primary user (PU). The proposed system is tested under two types of recovery algorithms: CoSaMP and SWOMP. The performance of the CS-based proposed matrix is measured using recovery error, mean square error (MSE) and probability of detection and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the CS-based proposed matrix has low recovery error and high performance detection under low SNR values and has low MSE with high compression.

Information decision support systems are becoming more in use as we are living in the era of digital data and the rise of artificial intelligence. Heart disease is one of the most known and dangerous is getting very important attention, this attention is translated into digital and prediction system that detects the presence of disease according to the available data and information. In this paper we propose a Heart Disease Prediction System using Machine Learning Algorithms, in terms of data we used the Cleveland dataset, this dataset is normalized then divided into three scenarios in terms of training and testing respectively, 80%-20%, 50%-50%, 3%-70%. In each case of the dataset if it is normalized or not we will have these three scenarios. We used three machine learning algorithms for every scenario mentioned before which support- vector machine (SVM), Sequential minimal optimization (SMO)and multilayer perceptron (MLP), in these algorithms we’ve used two different kernels to test the results upon that. These two types of simulation are added to the collection of scenarios mentioned above to become like the following we have at the main level two types normalized and unnormalized dataset, then for each one we have three types according to the amount of training and testing dataset, for each of these scenarios we have two scenarios according to the type of kernel to become 30 scenarios in total, our proposed system have shown dominance in terms of accuracy over the other previous works.

In this paper, a reconfigurable antenna design for 5G applications is presented. It is based on monopole antenna and fractal structure. The antenna is designed and simulated by using CST Microwave studio. The design structure consists of (monopole) feedline, ground plane, L-shape reflector, fractal structure and PIN diodes. The antenna is printed on (25×29×1.6mm3) FR-4 substrate ofεr= 4.3andtanδ= 0.001. The antenna shows a resonant frequency at 4.1 GHz with S11=-11.4 dB and an Omni-direction pattern of 1.21 dB gain. The L-shaped reflector is used to maintain the radiation pattern in a specific direction. Moreover, the proposed fractal structure is found to operate as a matching circuit to give another resonant frequency and enhance the antenna performance. Where it is used to give more manipulation in the antenna performance including frequency resonance and radiation patterns. The PIN-diodes are used to give many cases for more current manipulation. Moreover, the authors used RF (50ΩSMA port) between the monopole antenna and the right side of the ground plane to optimize directing radiation pattern and to eliminate the problems of interference between AC and DC that were produced from using PIN diode. This manipulation leads to change the resonant frequency and radiation pattern to the desired direction. So, all three parts are printed on a single side of the FR4 substrate with no metallization coating on the opposite side. Finally, the HFSS environment is invoked too for more results validation.