Author: Ibraheem Hatem Al-Dosari
Overview of Radio Propagation Models in the Millimeter Wave Range for Cellular Networks
Abstract: This paper presents a review of recent published articles on the propagation path loss measurements and results in the Millimeter-wave radio spectrum range and compares with known propagation models commonly applicable in the sub-6 GHz range. The review shows that path loss is a function of the extent of wireless network coverage. With heightened interest in the application of millimeter wave range as solution to the low wireless data rate, various propagation models have been modified and new ones developed across the industry and academia. It is observed that the known propagation models in the sub 6 GHz cannot be directly applied in the millimeter wave range without introducing some parameter modifications to the models. In the outdoor, the physical structures, like type of buildings, materials of the built and vegetation exact influence on the propagation models. Types of walls, metallic and wood structures in confinements also exhibited profound influence on the propagation models in indoor environments. In the papers reviewed, mobility was not considered. DOI: 10.1109/ICMEAS52683.2021.9739808
PSO Trained Hybrid Intelligent Classifier Using Wavelet and Statistical Features for Pipeline Leak Classification
Abstract— One of the main problems in the oil industrial field is the leakage in transporting pipelines due to its effect on human society , environment, and money loss. Therefore, the bottleneck for most researches in this subject is to minimize false alarm rate (FAR) for the adopted leak detection method. Although some recent methods succeed in classifying the existence or absence of the leak as a binary classification problem. But this paper proposed a novel leak detection technique which predicts the leak location and estimates its size within certain pre-defined ranges. In order to simulate the environmental conditions for real-time operating oil pipeline, accurate simulator known as OLGA program creates the oil physical parameters. Various methods for features extraction are considered such as statistical and wavelet techniques which are implemented to get the features from the fluid simulated waveforms. These features are organized and fed to an ANN classifier trained by PSO algorithm to determine the leak class out of 10 suggested classes. The proposed leak detection technique is used to simulate 18 kilometers belonging to Iraqi crude oil pipelines company operated in Baghdad. The achieved results of the true positive rate (TPR) for the proposed applied method for the leak detection and classification of different leak classes in terms of their positions and magnitudes were about 97%. Index Terms— Particle Swarm Optimization (PSO) , Leakage Detection , Artificial Neural Network (ANN), Hybrid Classifier. https://doi.org/10.33103/uot.ijccce.19.1.1
Machine Learning-Based Intelligent Video Surveillance in Smart City Framework
Abstract : The proposed method of using Machine Learning in Motion Detection and Pedestrian Tracking-assisted Intelligent Video Surveillance Systems (ML-IVSS) can be seen as an application of intelligent fusion techniques. ML-IVSS combines the power of motion detection, pedestrian tracking, and machine learning to create a more accurate and efficient surveillance system for smart cities. By fusing these techniques, ML-IVSS can effectively detect unusual behaviors such as trespassing, interruption, crime, or fall-down, and provide accurate depth data from surveillance footage to protect residents. Intelligent fusion techniques can help improve the accuracy and effectiveness of surveillance systems in smart cities, making them safer and more secure for residents. Combination channel models are used at first, and an object area with prominent features is selected for surveillance. Scaled modification and extraction of features are carried out on the presumed object’s region. Identifying the low-level characteristic is the first step in incorporating it into neural architectures for deep feature learning. A smart CCTV data set is used to evaluate the proposed method’s performance. According to the numerical analysis, the proposed ML-IVSS model outperforms other traditional approaches in terms of abnormal behaviour detection (98.8%), prediction (97.4%), accuracy (96.9%), F1-score (97.1%), precision (95.6%), and recall (96.2%). Keywords : Video Surveillance System; Machine Learning; Smart City; Intelligent Fusion Techniques; Deep Feature Learning. Doi : https://doi.org/10.54216/FPA.110203
Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification
Abstract : In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method’s Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study’s major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user’s genuine emotions during common computer-based tasks. Keywords : Artificial Intelligence , Facial Expressions; Deep fusion features; Smart Decision System; Behavior traits.
Implementation of Sawtooth Wavelet Thresholding for Noise Cancellation in One Dimensional Signal
The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance. Keywords: zigbee; wavelet transform; statistical features; wireless sensor network (wsn); classification https://doi.org/10.26552/com.C.2021.2.C44-C53 DOI: 10.26552/com.C.2021.2.C44-C53
A Squared-Chebyshev wavelet thresholding based 1D signal compression
Abstract In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression; but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches. https://doi.org/10.1016/j.dt.2018.08.009
A novel wavelet thresholding method for implementation of signal compressor design
ABSTRACT The paper deals with a work for unique wavelet shrinkage technique is planned and assessed in comparison with the opposite wavelet shrinkage ways like soft and onerous shrinkages. It’s evident that wavelet families have distinctive characteristics and details that successively provide them a unique behavior against an identical wave once they were utilized for a particular procedure. As an example, in signal process applications, like wave compression, a comparative analysis in conjugation with analysis study utilizing economical system was needed to accumulate appropriate results. During this work a wavelets analysis tool was utilized to compress an artificial wave. The purpose of this work is to style and execute another wavelets shrinkage strategy, so valuate it against alternative wavelets shrinkage ways, so as to seek out the best wavelet mum operations among the antecedently mentioned families. Conjointly associate applicable decomposition level has been found to utilize the novel shrinkage technique. This new shrinkage technique is employed to shrink the wavelets coefficients and yield a decent compressed pressure signal before transmit it. There are numerous evaluation parameters to make up the evaluation and analysis method for signal compression. But the additional well-known quantitative measures are: N.M.S.E. (normalized mean sq. error) and E.S.N.R. (enhancement of signal to noise ratio). These on top of mentioned performance indices are utilized to urge the comparison results among completely different shrinkage strategies. The planned shrinkage technique outperformed the opposite standard strategies for all elect decomposition levels and completely different threshold choice rules. As an example Hardy-sine has E.S.N.R. =10.26dB and N.M.S.E. =0.02 that is concerning four improvement in E.S.N.R. for onerous-sine as compared with regular hard technique. Later the conclusion is to highlight that; this planned technique may be utilized for wave compression in more studies. https://doi.org/10.1145/3321289.3321292