The communication networks (mobile phone networks, social media platforms) produce digital traces from their usages. This type of information help to understand and analyze the human mobility in very accurate way. By these analyzes over cities, it can give powerful data on daily citizen activities, urban planners have in that way, relevant indications for decision making on design and development. As well as, the Call detail Records (CDRs) provides valuable spatiotemporal data at the level of citywide or even nationwide. The CDRs could be analyzed to extract the life patterns and individuals mobility in an observed urban area and during ephemeral events. Whereas, their analysis gives conceptual views about human density and mobility patterns. In this study, the mobile phone traces concern an ephemeral event called Armada in Rouen city. However, important densities of individuals are analyzed and are represented to extract the life patterns by classifying the most active regions during observed period in this urban area. Then, the collective mobility patterns are investigated in aggregated urban mobility patterns via extracting the universal mobility law (power-law distribution). This investigation explores the characteristics of human mobility patterns, and model them mathematically depending on substantial parameters, that are the inter-event time, traveled distances (displacements), and the radius of gyration. The main purpose of this study is to determine the general pattern law of the population. And, its contribution is the resulting outcomes, which are revealed and visualized in both static and dynamic perspectives. They can be capitalized as guidelines to explore the urban pulse and life patterns. The numerical simulation results endorse the previous investigations. Hence, they found that the real system patterns almost follow an exponential distribution. Additionally, the experiments classified the mobility patterns into major classes as general, work, and off days. Keywords : Complex systems, urban, mobility, CDRs, mobile phone, spatio-temporal, network, radius of gyration, individual trajectory, city pulse, simulation, power-law distribution.
(1)
The electrocardiogram (ECG) is the recording of the electrical potential of the heart versus time. The analysis of ECG signals has been widely used in cardiac pathology to detect heart disease. The ECGs are non-stationary signals which are often contaminated by different types of noises from different sources. In this study, simulated noise models were proposed for the power-line interference (PLI), electromyogram (EMG) noise, base line wander (BW), white Gaussian noise (WGN) and composite noise. For suppressing noises and extracting the efficient morphology of an ECG signal, various processing techniques have been recently proposed. In this paper, wavelet transform (WT) is performed for noisy ECG signals. The graphical user interface (GUI)
... Show More
(1)
On Saturday, May 13, 2023, a glorious day was engraved in the history of Al-Kindy College Medical Journal as it is the day of indexing the journal in the Scopus Database Journals. The journal has paced a strenuous journey to make that achievement.
(1)
Wireless channels are typically much more noisy than wired links and subjected to fading due to multipath propagation which result in ISI and hence high error rate. Adaptive modulation is a powerful technique to improve the tradeoff between spectral efficiency and Bit Error Rate (BER). In order to adjust the transmission rate, channel state information (CSI) is required at the transmitter side.
In this paper the performance enhancement of using linear prediction along with channel estimation to track the channel variations and adaptive modulation were examined. The simulation results shows that the channel estimation is sufficient for low Doppler frequency shifts (<30 Hz), while channel prediction is much more suited at
... Show MoreThe Adaptive Optics technique has been developed to obtain the correction of atmospheric seeing. The purpose of this study is to use the MATLAB program to investigate the performance of an AO system with the most recent AO simulation tools, Objected-Oriented Matlab Adaptive Optics (OOMAO). This was achieved by studying the variables that impact image quality correction, such as observation wavelength bands, atmospheric parameters, telescope parameters, deformable mirror parameters, wavefront sensor parameters, and noise parameters. The results presented a detailed analysis of the factors that influence the image correction process as well as the impact of the AO components on that process
(6)
(1)
This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF)
... Show More
(19)
(15)
The regressor-based adaptive control is useful for controlling robotic systems with uncertain parameters but with known structure of robot dynamics. Unmodeled dynamics could lead to instability problems unless modification of control law is used. In addition, exact calculation of regressor for robots with more than 6 degrees of freedom is hard to be calculated, and the task could be more complex for robots. Whereas the adaptive approximation control is a powerful tool for controlling robotic systems with unmodeled dynamics. The local (partitioned) approximation-based adaptive control includes representation of the uncertain matrices and vectors in the robot model as finite combinations of basis functions. Update laws for the weighting matri
... Show More
(4)
(4)
(6)
(3)